Adaptive Invisible Steganography Leveraging Entropy-Based Image Encryption with the Mimic Octopus Adaptive Framework (MOAF)

Adaptive Invisible Steganography Leveraging Entropy-Based Image Encryption with the Mimic Octopus Adaptive Framework (MOAF) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adaptive Invisible Steganography Leveraging Entropy-Based Image Encryption with the Mimic Octopus Adaptive Framework (MOAF) Ms.Judy S, Dr.Rashmita Khilar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6300749/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Oct, 2025 Read the published version in Discover Computing → Version 1 posted 20 You are reading this latest preprint version Abstract In the realm of secure communication, invisible steganography plays a crucial role by allowing discreet data transmission within digital images while preserving their visual integrity. This study introduces a pioneering method aimed at enhancing the security and concealment of steganographic data. It achieves this by utilizing entropy-based image encryption in combination with a novel Mimic Octopus Adaptive Framework (MOAF). The primary objective of MOAF is to establish a higher level of image security, denoted as the third tier, by dynamically selecting suitable compression and encryption algorithms based on the entropy characteristics of the secret image. Our approach attained an impressive peak signal-to-noise ratio (PSNR) of 81.31 when comparing the secret image to the decompressed image, with a payload size of 79,747 bytes. Additionally, the structural similarity index (SSIM) achieved a perfect score of 1.0 under the same payload conditions. Comprehensive experiments and comparative analyses against established steganographic techniques demonstrate that our proposed system excels in security, imperceptibility, and resilience against various attacks. These findings underscore the potential of entropy-based image encryption, combined with the innovative Mimic Octopus Adaptive Framework (MOAF), to advance invisible steganography and enable more secure and clandestine data transmission in the digital age. Image Compression Image Pre-processing Steganography Cryptography Image Security Mimic Octopus Adaptive Framework Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Around the globe one of the most vital concerns of the healthcare organizations is cybersecurity. Medical imaging technologies depends upon picture archiving and communications systems (PACS) to record and send patient medical pictures. The medical images and the patient’s personal data is stored in the PACS server. Now healthcare workers can access the digital patient record remotely. Since cyber-attacks and data breaches on patient records are becoming more common, there is a requirement to protect the medical images in order to protect patients’ privacy [1,2]. The primary concern of this research is the security of the patient’s image records and to complicate the steganalysis process. When dealing with the images it is necessary to know about the types of images. There are various types of images such as binary images, grayscale images, colour images and multispectral images. When considering the medical images, they include the images of the human body parts such as X-rays, CT scans. MRI scans, Ultrasound images and microscopy images of the tissue samples. These images are strictly subject to regulations regarding their usage, storage and transmission in order to protect the patient’s privacy and also to ensure their accuracy and quality. The X-rays and CT scans are typically of grayscale whereas MRI scans and Ultrasound images can include colour information. Steganography is the practice of concealing a message or information within another file or medium, without anyone noticing that there is a hidden message. The possible inputs for steganography are: digital image or video file, audio file, text file, network packets, physical objects and cryptographic keys. Research is dealt considering the various types of inputs. In order to eliminate the colour distortion problem in the cover image, grayscale image is taken as input. Encryption and steganography are two distinct techniques used to secure information, but they can be used in combination to provide even greater security. Encryption involves transforming plaintext (i.e., readable information) into ciphertext (i.e., unreadable information) using an algorithm and a secret key. The ciphertext can only be decrypted back into plaintext by someone who has the correct key. This makes encryption a powerful tool for protecting the confidentiality of information. The chaotic encryption method is applied prior to merging the secret information and cover images [3]. This paper introduces a method, as outlined in reference [4], designed to securely and efficiently embed Electronic Health Records (EHR) with a high capacity into medical images within an Internet of Things (IoT) powered healthcare system. The anti-malware and protection mechanisms with advanced techniques such as code obfuscation are adopted as stealth techniques in applications by various researchers. To extract information from degraded images, Zero-steganography algorithm was proposed. Images usually get altered in the process of hiding information which is overcome by the zero-steganography algorithm [5]. Entropy of an image file is analysed in order to determine the optimal way to compress an image without much information loss. The concept of entropy is employed as the central factor in the novel ensemble Mimic Octopus Optimization Technique developed in this research. Picture entropy is considered to be a valuable statistic for image compression. Once the entropy is calculated, a high entropy value denotes a complex image and a low entropy value denotes a uniform image. This entropy can also be used to determine the quality, complexity and information content of an image. The remaining content of the article is structured as follows: In Section 2, we delve into the background study encompassing image compression, steganography, cryptography, and an examination of the limitations encountered in prior research efforts. Section 3 is dedicated to outlining the methodology, elucidating the architectural framework, and detailing the algorithm underpinning our proposed model. Moving on to Section 4, we engage in a comprehensive discussion regarding the results obtained, along with insightful analyses. Finally, Section 5 encapsulates our concluding remarks and outlines potential avenues for future enhancements and research endeavours. 2 Background Study The primary goal of image compression is to reduce the size of an image for effective storage and transmission. A colour image of size 640 × 480 requires approximately 1 MB space for storage. This image compression is achieved by pixel permutation which is nothing but rearranging or altering based on some criteria. The need of image compression arises when performing steganography. Since for better steganography it is required that the secret image should be of 0.4 bpp (bits per pixel) or lower. This image compression should also be performed without affecting the quality of the image. Compression and data transmission plays the pivotal role in the present healthcare technology which involves diagnosis through the medical imaging instruments [ 6 ]. Anirban Sengupta et.al aims to secure the JPEG compression processor which generates compressed medical images for further diagnosis. Segmentation is done as high intensity and low intensity part on the input image. The two main process that is involved in image compression are compression and de-compression. Figure 1 shows the various modules in the image compression and decompression process. The diagram shows the image compression and decompression process for efficient transmission. In the compression phase, the image undergoes transformation (e.g., discrete cosine or wavelet transform) to concentrate energy into fewer coefficients. The quantizer reduces the precision of these coefficients, and the entropy encoder compresses the data further using techniques like Huffman or arithmetic coding. The channel encoder adds redundancy to protect against transmission errors. In the decompression phase, the channel decoder corrects errors, and the entropy decoder reconstructs the quantized coefficients. The inverse quantizer restores the coefficients' original precision, and the inverse transformation converts them back to the spatial domain, producing the output image. This process allows the image to be accurately reconstructed, with some potential loss based on the compression ratio. The various categories of Image Compression techniques include lossless compression, image differencing, Fourier transform, and lossy compression. In this study, a preliminary examination of different algorithms within the domains of both lossless compression and lossy compression techniques has been conducted. The resulting comparison, based on compression ratios and processing times, has been presented in a tabular format. The optimized compression algorithm is used by the proposed model. Lossless image compression using Shift Coding and zigzag operations has been shown to provide accurate results [ 7 ]. A hybrid approach to color image compression combines Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD), and Run-Length Encoding (RLE), where each red, green, and blue component is processed separately [ 8 ]. A state-of-the-art survey on lossless image compression techniques highlights various methods and suggests the hybridization of arithmetic coding to enhance performance [ 9 ]. A novel high-frequency encoding algorithm incorporates DCT, high-frequency minimization, and a concurrent binary search algorithm, demonstrating superior performance compared to JPEG and JPEG2000 [ 10 ]. Additionally, an image compression approach using Adaptive Huffman Coding is proposed, with the potential for better transforms and filters to improve image quality in lossy compression scenarios [ 11 ]. A hybrid approach to color image encryption using hyper-chaotic systems and cellular automata leverages hyper-chaotic functions and non-uniform cellular automata for robust key image generation, offering strong resistance against noise and various attacks [ 12 ]. Visual cryptography techniques for secret share creation utilize multiple encryption and decryption stages, employing Elliptic Curve Cryptography (ECC) for encryption and the Rubik’s Cube algorithm for decryption, with implementation possible through MATLAB [ 13 ]. A novel high-capacity steganography method combines image-based ECC and Deep Neural Networks, significantly enhancing the anti-detection capability of the encrypted images [ 14 ]. This paper introduces a method, as outlined in reference [ 15 ], designed to securely and efficiently embed Electronic Health Records (EHR) with a high capacity into medical images within an Internet of Things (IoT) powered healthcare system. Medical image steganography involves hiding of data within medical images such as X-rays, CT scans, MRI scans, ultrasound images and others. The main purpose of medical image steganography is for securing medical research, protecting patient privacy and ensuring integrity of medical images. Image steganography based on Swim Transformer is incorporated with a novel embedding and extraction network [ 16 ]. Zhiyi Wang et. al., further used recursive permutation to scramble the secret image in-order to enhance security. The cover image and the secret image are prepared prior to embedding into stego image in the pre-processing module [ 17 ]. The LSB steganography embedding technique employed for concealing graphical content within monochrome images is rooted in the approach presented in reference [ 18 ]. This method represents a variation of the conventional LSB steganography, incorporating a fixed message image. Image Encryption Algorithm revolves around the three basic ideas: Pixel Permutation, Pixel Substitution and Visual Alteration [ 19 ]. The author performed encryption of the secret image before embedding the image onto the carrier image without any significant difference in visual effect of carrier. Messages are encrypted and the hiding process is by Noiseless Steganography [ 20 ]. Wahab et. al proposed a process, which involves encrypting the confidential message with RSA cryptography, followed by compression using Huffman's algorithm. Simultaneously, the cover image undergoes compression through the DWT algorithm. The resulting compressed cover image is then merged with the encrypted secret message using the LSB technique. Finally, this combined data is transmitted over the internet to its destination as a single compressed file [ 21 ]. This provided an assurance of secured mode of transmission of the secret image. Key management plays a pivotal role in the realm of cryptography. It's imperative to give meticulous attention to key management and the implementation of distribution mechanisms. These aspects are critical for safeguarding the security and dependability of cryptographic keys, covering secure key generation, exchange procedures, and storage protocols, as emphasized in reference [ 22 ]. 2.4 Limitations Based on the background study conducted on Image Compression, Steganography, and Cryptography algorithms, several limitations have been identified and can be outlined as follows: Challenge of developing algorithms that can efficiently handle high-resolution images in real-time scenarios [ 23 ] Loss of some information in the secret image [ 24 ] Even with good PSNR and SSIM scores, advanced steganalysis techniques may still detect the presence of the hidden message, particularly if patterns emerge in the stego-images that are detectable by statistical analysis Considering the background study and the identified limitations, a methodology is put forth with the aim of addressing these limitations effectively 3 Methodology The central concept behind this research was inspired by the defence mechanisms observed in the Mimic Octopus. This creature alters its coloration and body shape strategically, enabling it to defend itself and evade detection by potential predators. The fundamental idea drawn from the defence mechanism of the Mimic Octopus revolves around the use of a single crucial factor, such as entropy, as a key determinant. Utilizing this pivotal factor, which is the image's entropy, the compression technique and encryption algorithm have been incorporated. Consequently, this enhances the complexity of the steganalysis process. 3.1 Sender Network The architecture of the sender network is shown in Fig. 3 . The Novel Mimic Octopus Adaptive Framework starts by taking a medical image as its initial input, which serves as the secret image. This framework comprises several key components, including the Entropy Analyzer, Mimic Octopus Optimizer, Image Compressor, and Encryptor. In addition to the Mimic Octopus Adaptive Framework, there are three additional modules: the Pre-processor, Encoder, and Header Appender. 3.1.1 Entropy Analyzer The initial module is known as the Entropy Analyzer, responsible for the computation of entropy for the provided secret image file. Entropy measurement was employed to assess the level of information contained within an image [ 25 ]. Multiple approaches exist for computing image entropy. In this study, Shannon entropy is selected as the method to assess the entropy of the confidential image. The formula to calculate Shannon entropy H(V) for a discrete random variable V with probability distribution P(v i ) is: $$\:H\left(V\right)=-\sum\:_{x=1}^{n}P\left({v}_{x}\right)\:{log}_{2\:}\left(P\left({v}_{x}\right)\right)$$ 1 Where: H(V) is the entropy of random variable V n is the number of possible values V can take \(\:{v}_{x}\) represents each possible value of V \(\:P\left({v}_{x}\right)\) is the probability of V taking the value \(\:{v}_{x}\) Figure 3 shows the sample histogram generated along with the entropy values. 3.1.2 Mimic Octopus Optimizer The inputs for the Mimic Octopus Optimizer include both the entropy values and the secret image. The primary function of the Mimic Octopus Optimizer is to make determinations regarding which compression technique and encryption algorithm should be applied to the input secret image, based on the calculated entropy value. In this research, we have chosen to use two symmetric encryption algorithms: 3DES and AES. The key size for 3DES is 168 bits, which comprises three 56-bit keys. For AES, key sizes of 128, 192, or 256 bits are available. The encryption key is obtained from the sender after the entropy calculation has been performed. In this research, an examination of three lossy compression techniques—block truncation coding, vector quantization, and transform coding—is conducted using medical secret image files as input. The compression ratio and the process time is recorded and the overall comparative analysis is captured in the Table 1 . From Table 1 , it can be observed that the compression ratio of vector quantization is higher compared to the compression ratio of block truncation coding and transfer coding. Whereas the processing time for Vector Quantization is recoded to be very high. Transform Coding offers a compelling equilibrium between compression efficiency and maintaining perceptual quality, rendering it a sensible option when you aim to reduce image size without significantly compromising visual fidelity. Huffman coding can reduce file size by 10–50% by eliminating redundant information [ 23 ]. As a result, Transform Coding is employed for lossy compression, while Huffman Coding is utilized for lossless compression. Table 1 Comparative analysis of Compression Ratio and Process Time of Lossy Compression Sl. No Image Size Block Truncation Coding Vector Quantization Transform Coding CR PT CR PT CR PT 1 576 × 1280 0.08 0.03 1.05 23.54 0.39 0.13 2 1058×1600 0.16 0.05 1.88 49.21 1.16 0.19 3 1040 × 780 0.15 0.03 2.58 23.95 0.75 0.11 4 630 × 436 0.43 0.00 1.67 9.52 0.61 0.03 5 850 × 578 0.21 0.02 1.88 15.80 0.53 0.05 6 984 × 848 0.15 0.03 2.31 25.94 0.67 0.09 7 1058×1600 0.15 0.07 1.96 44.09 1.09 0.20 8 900 × 1225 0.06 0.05 1.25 31.01 0.41 0.19 9 1040 × 780 0.12 0.04 2.02 21.96 0.58 0.11 10 1280×1280 0.22 0.07 1.79 40.10 1.62 0.19 11 850 × 578 0.18 0.02 1.78 14.49 0.51 0.06 12 1200×1600 0.10 0.09 1.76 51.46 0.83 0.23 13 1280 × 960 0.08 0.04 1.52 32.36 0.69 0.16 14 720 × 1280 0.33 0.05 1.73 23.55 1.71 0.14 15 1040 × 468 0.28 0.04 2.17 13.27 0.86 0.08 When the entropy value is less than 5, the process involves employing Vector quantization lossy compression and the 3DES algorithm. Conversely, for an entropy value greater than 5, the procedure entails utilizing Huffman-coding lossless compression and AES encryption. 3.1.3 Pre-Processor A coloured JPG image is selected as the cover image. Before encoding, preprocessing is applied to this cover image. The main aim of pre-processing is to enhance the visual quality and the interpretability of the cover image. Given the various forms of extraneous information within the image represented by the image matrix, researchers have introduced numerous imaging techniques aimed at enhancing overall performance [ 26 ]. There are many ways to pre-process an image and, in this research, we have made an analysis of the various image enhancement and image filter techniques. From the analysis it is observed that PSNR value 42.84 obtained through 2D Frequency Shift Image Enhancement technique proposes a high signal-to-noise ratio and a good image quality. Hence the preprocessing technique for this research is opted to be the 2D Frequency Shift. The cover image is pre-processed using 2D Frequency Shift technique and the processed cover image becomes one of the inputs to the Encoder. 3.1.4 Encoder The Encoder processes both the encrypted secret image and the pre-processed cover image to perform the embedding operation. Utilizing the Least Significant Bit (LSB) Steganography technique, the Encoder hides the encrypted secret image within the cover image by altering the least significant bits of the cover image pixels. This method ensures that the visual integrity of the cover image remains largely intact while embedding the secret data. Specifically, each pixel of the cover image has its least significant bit modified to match the corresponding bit of the encrypted secret image. This process is repeated for all pixels until the entire encrypted secret image is hidden within the cover image, creating what is known as the stego-image. The resulting stego-image, which visually appears almost identical to the original cover image, now securely contains the concealed data. This stego-image is then passed as an input to the Header Appender module, where additional metadata or a digital header might be appended. This metadata can include information such as the encryption key, encoding algorithm used, or any other parameters necessary for the proper extraction and decryption of the hidden secret image during the decoding phase. 3.1.5 Header Appender The role of Header Appender is to ease the decoding and the decompression process at the receiver side. The Header Appender function augments the header section of the stego image with crucial information. This includes the entropy value of the secret image, the compression algorithm used, and the encryption algorithm applied. This enriched header information is incorporated into the stego image, creating the final output that is transmitted from the sender network to the receiver network. For reference, Table 2 presents the stego image along with the appended header details. 3.2 Receiver Network The architecture of the receiver network of the proposed model is shown in Fig. 5 . In the receiver network, the initial module is the Header Extractor, which is responsible for extracting the header from the stego image. The extracted header information is preserved for later use. Table 3 shows the graphical visualization of the extracted header information. Subsequently, the stego image is forwarded to the Decoder module. The Decoder's output is the encrypted image, which is then transmitted to the Decryptor module. The Decryptor utilizes the previously extracted header information to perform decryption. The Decryptor in-turn produces the compressed image with a valid. Finally, the Decompressor module decompresses the image, revealing the secret image for the receiver. 4 Results and Discussion 4.1 Experimental Results The dataset comprises medical images in JPG format obtained from https://www.kaggle.com/datasets/pallavisantha/pcod-ultrasound-images , and the implementation is conducted using Python 3.11.4, which is a 64-bit version of the language. Figure 6 presents a comparative analysis of image compression metrics, including Compression Ratio and PSNR (Peak Signal-to-Noise Ratio) values, between the proposed model and the methods mentioned in the respective reference numbers. This analysis demonstrates that the proposed model achieves a lower Compression Ratio (CR) and a higher Peak Signal-to-Noise Ratio (PSNR) value compared to other methods. Figure 7 is obtained by performing all the stages of lossless image compression on the secret image. The corresponding Histogram is also obtained and it is listed in the Figure below. Table 4 shows that analysis of the image enhancement like 2D frequency shift, Adaptive Histogram Equalization, CLAHE and the filters like Bilateral, Gaussian and Median Blur, along with the performance metric of each. The 2D Frequency shift method is chosen as the pre-processing technique for the cover image, resulting in a PSNR value of 42.84. Table 4 Analysis of Image Enhancement and Filtering Techniques Metric Image Enhancement and Filter Performance 2d Frequency Shift AHE CLAHE Bilateral Gaussian Median Blur MSE 3.37 70.72 87.38 49.78 91.69 89.82 SNR 33.53 20.31 19.40 21.85 19.19 19.40 PSNR 42.84 29.63 28.72 31.15 28.50 28.72 SSIM 0.9988 0.9606 0.8935 0.8935 0.7434 0.8934 Figure 8 Shows the processed cover image, compressed secret image, encrypted compressed secret image and the stego image which is obtained after encoding. The corresponding histograms are also generated and shown in the figure. 4.2 Calculating Performance Metrics Evaluating the efficacy of steganography techniques in the analysis of stego images encompasses a range of performance metrics. These metrics serve as valuable indicators for assessing the stego images' quality, security, and their ability to withstand detection efforts. While numerous metrics exist, our research focused specifically on the examination of PSNR, MSE, and SSIM as key indicators for our analysis of the stego images. Below are the formulas and steps to calculate the performance metrics PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Square Error), and SSIM (Structural Similarity Index) for analysing stego images: a. Peak Signal-to-Noise Ratio (PSNR): PSNR measures the quality of a reconstructed or compressed signal concerning the original signal. It can be represented as: PSNR (dB) = 10 * log10((MAX^2) / MSE) In this equation: MAX denotes the maximum attainable pixel value (e.g., 255 for an 8-bit image). MSE stands for the Mean Squared Error. b. Mean Squared Error (MSE): MSE calculates the average squared difference between the original and reconstructed signals or images. It can be computed as: MSE = (1 / (row * col)) * Σ [ Σ (P_original - P_reconstructed) ^2] In this equation: row and col represent the dimensions of the image. P_original refers to the pixel value in the original image. P_reconstructed corresponds to the pixel value in the reconstructed image. c. Structural Similarity Index (SSIM): SSIM is a metric that quantifies the structural similarity between two images, considering luminance, contrast, and structure. It can be expressed as: SSIM (a, b) = [(2 * µ_a * µ_b + k1) * (2 * σ_ab + k2)] / [(µ_a^2 + µ_b^2 + k1) * (σ_a^2 + σ_b^2 + k2)] In this equation: a and b denote the two images being compared. µ_a and µ_b represents the means of a and b, respectively. σ_a and σ_b signifies the standard deviations of a and b, respectively. σ_ab stands for the covariance of a and b. k1 and k2 are small constants included to avoid division by zero and ensure the stability of the formula. The performance metrics are computed by comparing the original secret image with the output generated by the Decompressor module, as indicated in Fig. 9 . In the study conducted by De Rosal Igantius Moses Setiadi et al. [ 27 ], they reported PSNR values of 51.1449 after LSB (Least Significant Bit) and 36.0354 after PVD (Pixel Value Differencing) calculations. In their paper [ 28 ], a PSNR value of 65.5 was specifically noted for the Lena image. In our own research, we achieved a maximum PSNR value of 81.31 when comparing the secret image with the decompressed image, using a payload of 79,747 bytes. Additionally, the SSIM (Structural Similarity Index) achieved a perfect score of 1.0 for the same payload. 5 Conclusion and Future Enhancement The proposed approach employs a unique framework called the Mimic Octopus Adaptive Framework (MOAF), which is designed to enhance the intricacy of the steganalysis process. This objective is accomplished by introducing variation in compression and encryption outcomes, with the entropy of the image acting as the triggering factor. In this research, the MOAF (Mimic Octopus Adaptive Framework) framework has been effectively employed, starting with Shannon entropy-based entropy calculation to understand image information content. Lossless compression using Transform coding achieved a Compression Ratio (CR) of 1.16 and required minimal Processing Time (PT), preserving perceptual quality while reducing image size. Huffman coding further reduced file size in the lossy compression phase. Robust image security was ensured through encryption with 3DES and AES, yielding an impressive Peak Signal-to-Noise Ratio (PSNR) of 100 dB when comparing the decrypted image with the original. Additionally, LSB Steganography in the Encoder module demonstrated a maximum PSNR of 81.31, indicating the ability to embed data with minimal perceptual distortion. These findings collectively underscore the MOAF framework's capacity to balance compression efficiency, data security, and perceptual quality, making it a promising solution for medical image processing and secure transmission while maintaining efficient processing times. In summary, taking into account all the implementations and results discussed, it becomes evident that while steganalysis has the potential to detect the presence of a concealed image within a cover image, our optimization technique has notably increased the complexity involved in recovering the secret image. This heightened complexity results from the utilization of a diverse set of compression and encryption algorithms within our approach. Our future plans entail integrating additional encryption algorithms into the ensemble mimic octopus encryption framework. The objective is to generate a greater diversity of outcomes, thereby increasing the complexity of steganalysis. Declarations ACKNOWLEDGEMENT Ethical Approval: The dataset used in this study comprises de-identified medical ultrasound images and was obtained from a publicly available source on Kaggle: https://www.kaggle.com/datasets/pallavisantha/pcod-ultrasound-images. No administrative permissions were required to access or use the dataset. As this study did not involve direct interaction with human participants or the collection of new human data, approval from an institutional ethics committee was not required. According to the information provided by the original dataset creators, all experimental protocols were approved by the relevant institutional review board, and informed consent was obtained from all participants, including from parents or legally authorized representatives for subjects under the age of 16. All data usage adhered to relevant ethical guidelines and regulations, including those outlined in the Declaration of Helsinki. Author Contribution: Judy S conceptualized the research idea, developed the methodology, performed the experiments, and carried out the data analysis. She also drafted the initial manuscript. Dr. Rashmita Khilar provided guidance throughout the research process, supervised the project, contributed to refining the methodology, and critically reviewed and edited the manuscript. Both authors read and approved the final version of the manuscript. Consent to Participate: Not applicable for this section Consent to Publish: Not applicable for this section Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding: There is no funding available. DATA AVAILABILITY The dataset utilized in this study was sourced from the publicly accessible Kaggle repository, specifically from the PCOD Ultrasound Images dataset curated by Pallavi Santha. It can be accessed at the following URL: https://www.kaggle.com/datasets/pallavisantha /pcod-ultrasound-images.. 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Qin, “A New High Capacity Image Steganography Method Combined With Image Elliptic Curve Cryptography and Deep Neural Network,” IEEE Access , vol. 8, pp. 25777–25788, 2020, doi: https://doi.org/10.1109/access.2020.2971528. S. A. Parah, J. A. Sheikh, J. A. Akhoon, and N. A. Loan, “Electronic Health Record hiding in Images for smart city applications: A computationally efficient and reversible information hiding technique for secure communication,” Future Generation Computer Systems , vol. 108, pp. 935–949, Jul. 2020, doi: https://doi.org/10.1016/j.future.2018.02.023. Z. Wang, M. Zhou, B.-J. Liu, and T. Li, “Deep Image Steganography Using Transformer and Recursive Permutation,” Entropy , vol. 24, no. 7, pp. 878–878, Jun. 2022, doi: https://doi.org/10.3390/e24070878. N. Subramanian, I. Cheheb, O. Elharrouss, S. Al-Maadeed, and A. Bouridane, “End-to-End Image Steganography Using Deep Convolutional Autoencoders,” IEEE Access , vol. 9, pp. 135585–135593, 2021, doi: https://doi.org/10.1109/access.2021.3113953. J. D. Miranda and D. J. Parada, “LSB steganography detection in monochromatic still images using artificial neural networks,” Multimedia Tools and Applications , vol. 81, no. 1, pp. 785–805, Sep. 2021, doi: https://doi.org/10.1007/s11042-021-11527-2. K. Sharma, A. Aggarwal, T. Singhania, D. Gupta, and A. Khanna, “Hiding Data in Images Using Cryptography and Deep Neural Network,” Journal of Artificial Intelligence and Systems , vol. 1, no. 1, pp. 143–162, 2019, doi: https://doi.org/10.33969/ais.2019.11009. J. Juhari and M. F. Andrean, “On the Application of Noiseless Steganography and Elliptic Curves Cryptography Digital Signature Algorithm Methods in Securing Text Messages,” CAUCHY: Jurnal Matematika Murni dan Aplikasi , vol. 7, no. 3, pp. 483–492, Oct. 2022, doi: https://doi.org/10.18860/ca.v7i3.17358. O. F. A. Wahab, A. A. M. Khalaf, A. I. Hussein, and H. F. A. Hamed, “Hiding Data Using Efficient Combination of RSA Cryptography, and Compression Steganography Techniques,” IEEE Access , vol. 9, pp. 31805–31815, 2021, doi: https://doi.org/10.1109/access.2021.3060317. L. Feng, J. Du, C. Fu, and W. Song, “Image Encryption Algorithm Combining Chaotic Image Encryption and Convolutional Neural Network,” Electronics, vol. 12, no. 16, pp. 3455–3455, Aug. 2023, doi: https://doi.org/10.3390/electronics12163455. Saja Hikmat Dawood, Saadi Mohammed Saadi, and Israa Shihab Ahmed, “Subject Review: Various Techniques for Image Compression,” International journal of scientific research in science, engineering and technology , pp. 222–235, Aug. 2023, doi: https://doi.org/10.32628/ijsrset23103194. H. S. Prasantha, H. L. Shashidhara, and K. N. Balasubramanya Murthy, “Image Compression Using SVD,” IEEE Xplore , Dec. 01, 2007. https://ieeexplore.ieee.org/ document/4426357 (accessed May 24, 2022). Bilal Bataineh, “Image contrast enhancement for preserving entropy and image visual features,” International Journal of Advances in Intelligent Informatics , vol. 9, no. 2, pp. 161–161, Jul. 2023, doi: https://doi.org/10.26555/ijain.v9i2.907. D. He and S. Xiong, “Image Processing Design and Algorithm Research Based on Cloud Computing,” Journal of Sensors , vol. 2021, p. e9198884, Oct. 2021, doi: https://doi.org/10.1155/2021/9198884. E. Z. Astuti, M. Setiadi, Eko Hari Rachmawanto, Christy Atika Sari, and K. Sarker, “LSB-based Bit Flipping Methods for Color Image Steganography,” vol. 1501, no. 1, pp. 012019–012019, Mar. 2020, doi: https://doi.org/10.1088/1742-6596/1501/1/012019. S. Alsamaraee and A. S. Ali, “A crypto-steganography scheme for IoT applications based on bit interchange and crypto-system,” Bulletin of Electrical Engineering and Informatics , vol. 11, no. 6, pp. 3539–3550, Dec. 2022, doi: https://doi.org/10.11591/eei.v11i6.4194. Additional Declarations No competing interests reported. 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Decompression Process\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6300749/v1/ba10bad50e51763c19d772e3.png"},{"id":82276865,"identity":"fc90f259-6731-4b86-a7ca-5aeb91dbe830","added_by":"auto","created_at":"2025-05-08 14:48:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":220754,"visible":true,"origin":"","legend":"\u003cp\u003eSender Network of Ensemble Mimic Octopus Optimization Architecture\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6300749/v1/8001b9ccd0005cff499a6d74.png"},{"id":82278476,"identity":"1a9b24d1-dac7-4158-8431-9180e263699f","added_by":"auto","created_at":"2025-05-08 14:56:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75204,"visible":true,"origin":"","legend":"\u003cp\u003eShannon entropy calculation of Secret Image\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6300749/v1/6bd7ef8329b83988a2ffeca1.png"},{"id":82276870,"identity":"e4fe22b9-9442-458a-a73c-61dc06bbff86","added_by":"auto","created_at":"2025-05-08 14:48:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161551,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 5.\u003c/strong\u003e Receiver Network of Ensemble Mimic Octopus Optimization Architecture\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6300749/v1/daf4745395b7eef45c91c537.png"},{"id":82276876,"identity":"e8c33c64-681c-47a0-9422-718e57e5fa9d","added_by":"auto","created_at":"2025-05-08 14:48:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":72781,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 6\u003c/strong\u003e Comparative Analysis of Compression Ratio (CR) and Peak Signal-to-Noise Ratio (PSNR) for Existing and Proposed Methods\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6300749/v1/8c1719c92e2708196b35b06a.png"},{"id":82278477,"identity":"8292f6b3-f8c6-486e-b733-e12444a87648","added_by":"auto","created_at":"2025-05-08 14:56:24","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":799687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 7\u003c/strong\u003e Huffman-coding lossless image compressed images depicting the steps of compression with their corresponding Histograms\u003c/p\u003e","description":"","filename":"6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6300749/v1/a021f68d03eb0ccd20e321b4.jpeg"},{"id":82276881,"identity":"63cc9e88-0d4f-46fa-a065-088c0c651a9f","added_by":"auto","created_at":"2025-05-08 14:48:24","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":789844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 8 \u003c/strong\u003eImages depicting the Compression, Encryption and Encoding Process\u003c/p\u003e","description":"","filename":"7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6300749/v1/615f3d7aa06a7f6c1f668cf9.jpeg"},{"id":82278485,"identity":"e3f81567-e05d-4572-a2bb-897722fcb87f","added_by":"auto","created_at":"2025-05-08 14:56:25","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":150168,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 9 \u003c/strong\u003eSimilarity Metric Comparison of Secret Image and Decompressed Image\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6300749/v1/45f38c2b2484fc7ba36a1525.png"},{"id":93419748,"identity":"378b1046-c868-4671-bee7-73ed313a37cc","added_by":"auto","created_at":"2025-10-13 16:07:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3256960,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6300749/v1/6f26f665-5ee3-468e-9e35-81ff45a42cc4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive Invisible Steganography Leveraging Entropy-Based Image Encryption with the Mimic Octopus Adaptive Framework (MOAF)","fulltext":[{"header":"1\tIntroduction","content":"\u003cp\u003eAround the globe one of the most vital concerns of the healthcare organizations is cybersecurity. Medical imaging technologies depends upon picture archiving and communications systems (PACS) to record and send patient medical pictures. The medical images and the patient\u0026rsquo;s personal data is stored in the PACS server. Now healthcare workers can access the digital patient record remotely. Since cyber-attacks and data breaches on patient records are becoming more common, there is a requirement to protect the medical images in order to protect patients\u0026rsquo; privacy [1,2].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe primary concern of this research is the security of the patient\u0026rsquo;s image records and to complicate the steganalysis process. When dealing with the images it is necessary to know about the types of images. There are various types of images such as binary images, grayscale images, colour images and multispectral images. When considering the medical images, they include the images of the human body parts such as X-rays, CT scans. MRI scans, Ultrasound images and microscopy images of the tissue samples. These images are strictly subject to regulations regarding their usage, storage and transmission in order to protect the patient\u0026rsquo;s privacy and also to ensure their accuracy and quality. The X-rays and CT scans are typically of grayscale whereas MRI scans and Ultrasound images can include colour information.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSteganography is the practice of concealing a message or information within another file or medium, without anyone noticing that there is a hidden message. The possible inputs for steganography are: digital image or video file, audio file, text file, network packets, physical objects and cryptographic keys. Research is dealt considering the various types of inputs. In order to eliminate the colour distortion problem in the cover image, grayscale image is taken as input. Encryption and steganography are two distinct techniques used to secure information, but they can be used in combination to provide even greater security. Encryption involves transforming plaintext (i.e., readable information) into ciphertext (i.e., unreadable information) using an algorithm and a secret key. The ciphertext can only be decrypted back into plaintext by someone who has the correct key. This makes encryption a powerful tool for protecting the confidentiality of information. The chaotic encryption method is applied prior to merging the secret information and cover images [3]. This paper introduces a method, as outlined in reference [4], designed to securely and efficiently embed Electronic Health Records (EHR) with a high capacity into medical images within an Internet of Things (IoT) powered healthcare system. The anti-malware and protection mechanisms with advanced techniques such as code obfuscation are adopted as stealth techniques in applications by various researchers. To extract information from degraded images, Zero-steganography algorithm was proposed. Images usually get altered in the process of hiding information which is overcome by the zero-steganography algorithm \u0026nbsp;[5].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEntropy of an image file is analysed in order to determine the optimal way to compress an image without much information loss. The concept of entropy is employed as the central factor in the novel ensemble Mimic Octopus Optimization Technique developed in this research. Picture entropy is considered to be a valuable statistic for image compression. Once the entropy is calculated, a high entropy value denotes a complex image and a low entropy value denotes a uniform image. This entropy can also be used to determine the quality, complexity and information content of an image.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe remaining content of the article is structured as follows: In Section 2, we delve into the background study encompassing image compression, steganography, cryptography, and an examination of the limitations encountered in prior research efforts. Section 3 is dedicated to outlining the methodology, elucidating the architectural framework, and detailing the algorithm underpinning our proposed model. Moving on to Section 4, we engage in a comprehensive discussion regarding the results obtained, along with insightful analyses. Finally, Section 5 encapsulates our concluding remarks and outlines potential avenues for future enhancements and research endeavours.\u003c/p\u003e"},{"header":"2 Background Study","content":"\u003cp\u003eThe primary goal of image compression is to reduce the size of an image for effective storage and transmission. A colour image of size 640 \u0026times; 480 requires approximately 1 MB space for storage. This image compression is achieved by pixel permutation which is nothing but rearranging or altering based on some criteria. The need of image compression arises when performing steganography. Since for better steganography it is required that the secret image should be of 0.4 bpp (bits per pixel) or lower. This image compression should also be performed without affecting the quality of the image. Compression and data transmission plays the pivotal role in the present healthcare technology which involves diagnosis through the medical imaging instruments [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Anirban Sengupta et.al aims to secure the JPEG compression processor which generates compressed medical images for further diagnosis. Segmentation is done as high intensity and low intensity part on the input image.\u003c/p\u003e \u003cp\u003eThe two main process that is involved in image compression are compression and de-compression. Figure\u0026nbsp;1 shows the various modules in the image compression and decompression process. The diagram shows the image compression and decompression process for efficient transmission. In the compression phase, the image undergoes transformation (e.g., discrete cosine or wavelet transform) to concentrate energy into fewer coefficients. The quantizer reduces the precision of these coefficients, and the entropy encoder compresses the data further using techniques like Huffman or arithmetic coding. The channel encoder adds redundancy to protect against transmission errors.\u003c/p\u003e \u003cp\u003eIn the decompression phase, the channel decoder corrects errors, and the entropy decoder reconstructs the quantized coefficients. The inverse quantizer restores the coefficients' original precision, and the inverse transformation converts them back to the spatial domain, producing the output image. This process allows the image to be accurately reconstructed, with some potential loss based on the compression ratio.\u003c/p\u003e \u003cp\u003eThe various categories of Image Compression techniques include lossless compression, image differencing, Fourier transform, and lossy compression. In this study, a preliminary examination of different algorithms within the domains of both lossless compression and lossy compression techniques has been conducted. The resulting comparison, based on compression ratios and processing times, has been presented in a tabular format. The optimized compression algorithm is used by the proposed model. Lossless image compression using Shift Coding and zigzag operations has been shown to provide accurate results [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A hybrid approach to color image compression combines Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD), and Run-Length Encoding (RLE), where each red, green, and blue component is processed separately [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A state-of-the-art survey on lossless image compression techniques highlights various methods and suggests the hybridization of arithmetic coding to enhance performance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A novel high-frequency encoding algorithm incorporates DCT, high-frequency minimization, and a concurrent binary search algorithm, demonstrating superior performance compared to JPEG and JPEG2000 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, an image compression approach using Adaptive Huffman Coding is proposed, with the potential for better transforms and filters to improve image quality in lossy compression scenarios [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA hybrid approach to color image encryption using hyper-chaotic systems and cellular automata leverages hyper-chaotic functions and non-uniform cellular automata for robust key image generation, offering strong resistance against noise and various attacks [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Visual cryptography techniques for secret share creation utilize multiple encryption and decryption stages, employing Elliptic Curve Cryptography (ECC) for encryption and the Rubik\u0026rsquo;s Cube algorithm for decryption, with implementation possible through MATLAB [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A novel high-capacity steganography method combines image-based ECC and Deep Neural Networks, significantly enhancing the anti-detection capability of the encrypted images [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This paper introduces a method, as outlined in reference [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], designed to securely and efficiently embed Electronic Health Records (EHR) with a high capacity into medical images within an Internet of Things (IoT) powered healthcare system.\u003c/p\u003e \u003cp\u003eMedical image steganography involves hiding of data within medical images such as X-rays, CT scans, MRI scans, ultrasound images and others. The main purpose of medical image steganography is for securing medical research, protecting patient privacy and ensuring integrity of medical images. Image steganography based on Swim Transformer is incorporated with a novel embedding and extraction network [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Zhiyi Wang et. al., further used recursive permutation to scramble the secret image in-order to enhance security. The cover image and the secret image are prepared prior to embedding into stego image in the pre-processing module [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The LSB steganography embedding technique employed for concealing graphical content within monochrome images is rooted in the approach presented in reference [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This method represents a variation of the conventional LSB steganography, incorporating a fixed message image.\u003c/p\u003e \u003cp\u003eImage Encryption Algorithm revolves around the three basic ideas: Pixel Permutation, Pixel Substitution and Visual Alteration [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The author performed encryption of the secret image before embedding the image onto the carrier image without any significant difference in visual effect of carrier. Messages are encrypted and the hiding process is by Noiseless Steganography [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Wahab et. al proposed a process, which involves encrypting the confidential message with RSA cryptography, followed by compression using Huffman's algorithm. Simultaneously, the cover image undergoes compression through the DWT algorithm. The resulting compressed cover image is then merged with the encrypted secret message using the LSB technique. Finally, this combined data is transmitted over the internet to its destination as a single compressed file [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This provided an assurance of secured mode of transmission of the secret image. Key management plays a pivotal role in the realm of cryptography. It's imperative to give meticulous attention to key management and the implementation of distribution mechanisms. These aspects are critical for safeguarding the security and dependability of cryptographic keys, covering secure key generation, exchange procedures, and storage protocols, as emphasized in reference [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Limitations\u003c/h2\u003e \u003cp\u003eBased on the background study conducted on Image Compression, Steganography, and Cryptography algorithms, several limitations have been identified and can be outlined as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eChallenge of developing algorithms that can efficiently handle high-resolution images in real-time scenarios [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLoss of some information in the secret image [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEven with good PSNR and SSIM scores, advanced steganalysis techniques may still detect the presence of the hidden message, particularly if patterns emerge in the stego-images that are detectable by statistical analysis\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eConsidering the background study and the identified limitations, a methodology is put forth with the aim of addressing these limitations effectively\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Methodology","content":"\u003cp\u003eThe central concept behind this research was inspired by the defence mechanisms observed in the Mimic Octopus. This creature alters its coloration and body shape strategically, enabling it to defend itself and evade detection by potential predators. The fundamental idea drawn from the defence mechanism of the Mimic Octopus revolves around the use of a single crucial factor, such as entropy, as a key determinant. Utilizing this pivotal factor, which is the image's entropy, the compression technique and encryption algorithm have been incorporated. Consequently, this enhances the complexity of the steganalysis process.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sender Network\u003c/h2\u003e \u003cp\u003eThe architecture of the sender network is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The Novel Mimic Octopus Adaptive Framework starts by taking a medical image as its initial input, which serves as the secret image. This framework comprises several key components, including the Entropy Analyzer, Mimic Octopus Optimizer, Image Compressor, and Encryptor. In addition to the Mimic Octopus Adaptive Framework, there are three additional modules: the Pre-processor, Encoder, and Header Appender.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Entropy Analyzer\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe initial module is known as the Entropy Analyzer, responsible for the computation of entropy for the provided secret image file. Entropy measurement was employed to assess the level of information contained within an image [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Multiple approaches exist for computing image entropy. In this study, Shannon entropy is selected as the method to assess the entropy of the confidential image.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe formula to calculate Shannon entropy H(V) for a discrete random variable V with probability distribution P(v\u003csub\u003ei\u003c/sub\u003e) is:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:H\\left(V\\right)=-\\sum\\:_{x=1}^{n}P\\left({v}_{x}\\right)\\:{log}_{2\\:}\\left(P\\left({v}_{x}\\right)\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eH(V) is the entropy of random variable V\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003en is the number of possible values V can take\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{v}_{x}\\)\u003c/span\u003e \u003c/span\u003e represents each possible value of V\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:P\\left({v}_{x}\\right)\\)\u003c/span\u003e \u003c/span\u003e is the probability of V taking the value \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{v}_{x}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the sample histogram generated along with the entropy values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Mimic Octopus Optimizer\u003c/h2\u003e \u003cp\u003eThe inputs for the Mimic Octopus Optimizer include both the entropy values and the secret image. The primary function of the Mimic Octopus Optimizer is to make determinations regarding which compression technique and encryption algorithm should be applied to the input secret image, based on the calculated entropy value. In this research, we have chosen to use two symmetric encryption algorithms: 3DES and AES. The key size for 3DES is 168 bits, which comprises three 56-bit keys. For AES, key sizes of 128, 192, or 256 bits are available. The encryption key is obtained from the sender after the entropy calculation has been performed.\u003c/p\u003e \u003cp\u003eIn this research, an examination of three lossy compression techniques\u0026mdash;block truncation coding, vector quantization, and transform coding\u0026mdash;is conducted using medical secret image files as input. The compression ratio and the process time is recorded and the overall comparative analysis is captured in the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. From Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, it can be observed that the compression ratio of vector quantization is higher compared to the compression ratio of block truncation coding and transfer coding. Whereas the processing time for Vector Quantization is recoded to be very high. Transform Coding offers a compelling equilibrium between compression efficiency and maintaining perceptual quality, rendering it a sensible option when you aim to reduce image size without significantly compromising visual fidelity. Huffman coding can reduce file size by 10\u0026ndash;50% by eliminating redundant information [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. As a result, Transform Coding is employed for lossy compression, while Huffman Coding is utilized for lossless compression.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative analysis of Compression Ratio and Process Time of Lossy Compression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSl. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eImage Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eBlock Truncation Coding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eVector Quantization\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eTransform Coding\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e576 \u0026times; 1280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e1058\u0026times;1600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e1040 \u0026times; 780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e630 \u0026times; 436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e850 \u0026times; 578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e984 \u0026times; 848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e1058\u0026times;1600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e900 \u0026times; 1225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e1040 \u0026times; 780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e1280\u0026times;1280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e850 \u0026times; 578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e1200\u0026times;1600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e1280 \u0026times; 960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e720 \u0026times; 1280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e1040 \u0026times; 468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen the entropy value is less than 5, the process involves employing Vector quantization lossy compression and the 3DES algorithm. Conversely, for an entropy value greater than 5, the procedure entails utilizing Huffman-coding lossless compression and AES encryption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Pre-Processor\u003c/h2\u003e \u003cp\u003eA coloured JPG image is selected as the cover image. Before encoding, preprocessing is applied to this cover image. The main aim of pre-processing is to enhance the visual quality and the interpretability of the cover image. Given the various forms of extraneous information within the image represented by the image matrix, researchers have introduced numerous imaging techniques aimed at enhancing overall performance [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. There are many ways to pre-process an image and, in this research, we have made an analysis of the various image enhancement and image filter techniques. From the analysis it is observed that PSNR value 42.84 obtained through 2D Frequency Shift Image Enhancement technique proposes a high signal-to-noise ratio and a good image quality. Hence the preprocessing technique for this research is opted to be the 2D Frequency Shift. The cover image is pre-processed using 2D Frequency Shift technique and the processed cover image becomes one of the inputs to the Encoder.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4 Encoder\u003c/h2\u003e \u003cp\u003eThe Encoder processes both the encrypted secret image and the pre-processed cover image to perform the embedding operation. Utilizing the Least Significant Bit (LSB) Steganography technique, the Encoder hides the encrypted secret image within the cover image by altering the least significant bits of the cover image pixels. This method ensures that the visual integrity of the cover image remains largely intact while embedding the secret data. Specifically, each pixel of the cover image has its least significant bit modified to match the corresponding bit of the encrypted secret image. This process is repeated for all pixels until the entire encrypted secret image is hidden within the cover image, creating what is known as the stego-image. The resulting stego-image, which visually appears almost identical to the original cover image, now securely contains the concealed data. This stego-image is then passed as an input to the Header Appender module, where additional metadata or a digital header might be appended. This metadata can include information such as the encryption key, encoding algorithm used, or any other parameters necessary for the proper extraction and decryption of the hidden secret image during the decoding phase.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.5 Header Appender\u003c/h2\u003e \u003cp\u003eThe role of Header Appender is to ease the decoding and the decompression process at the receiver side. The Header Appender function augments the header section of the stego image with crucial information. This includes the entropy value of the secret image, the compression algorithm used, and the encryption algorithm applied. This enriched header information is incorporated into the stego image, creating the final output that is transmitted from the sender network to the receiver network. For reference, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the stego image along with the appended header details.\u003c/p\u003e \n\u003cp\u003e\u003cimg 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\" width=\"582\" height=\"199\"\u003e\u003c/p\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Receiver Network\u003c/h2\u003e \u003cp\u003eThe architecture of the receiver network of the proposed model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e. In the receiver network, the initial module is the Header Extractor, which is responsible for extracting the header from the stego image. The extracted header information is preserved for later use. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the graphical visualization of the extracted header information. Subsequently, the stego image is forwarded to the Decoder module. The Decoder's output is the encrypted image, which is then transmitted to the Decryptor module. The Decryptor utilizes the previously extracted header information to perform decryption. The Decryptor in-turn produces the compressed image with a valid. Finally, the Decompressor module decompresses the image, revealing the secret image for the receiver.\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"608\" height=\"272\"\u003e\u003c/p\u003e"},{"header":"4 Results and Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Experimental Results\u003c/h2\u003e \u003cp\u003eThe dataset comprises medical images in JPG format obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kaggle.com/datasets/pallavisantha/pcod-ultrasound-images\u003c/span\u003e\u003cspan address=\"https://www.kaggle.com/datasets/pallavisantha/pcod-ultrasound-images\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, and the implementation is conducted using Python 3.11.4, which is a 64-bit version of the language. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents a comparative analysis of image compression metrics, including Compression Ratio and PSNR (Peak Signal-to-Noise Ratio) values, between the proposed model and the methods mentioned in the respective reference numbers. This analysis demonstrates that the proposed model achieves a lower Compression Ratio (CR) and a higher Peak Signal-to-Noise Ratio (PSNR) value compared to other methods.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003e is obtained by performing all the stages of lossless image compression on the secret image. The corresponding Histogram is also obtained and it is listed in the Figure below. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that analysis of the image enhancement like 2D frequency shift, Adaptive Histogram Equalization, CLAHE and the filters like Bilateral, Gaussian and Median Blur, along with the performance metric of each. The 2D Frequency shift method is chosen as the pre-processing technique for the cover image, resulting in a PSNR value of 42.84.\u003c/p\u003e \n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Analysis of Image Enhancement and Filtering Techniques\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"557\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 491px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImage Enhancement and Filter Performance\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2d Frequency\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eShift\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAHE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCLAHE\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBilateral\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGaussian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBlur\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e70.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e87.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e49.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e91.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e89.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e33.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e20.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e19.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e21.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e19.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e19.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSNR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e42.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e29.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e28.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e31.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e28.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e28.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSIM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.9988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.9606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.8935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.8935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.7434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.8934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e Shows the processed cover image, compressed secret image, encrypted compressed secret image and the stego image which is obtained after encoding. The corresponding histograms are also generated and shown in the figure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Calculating Performance Metrics\u003c/h2\u003e \u003cp\u003eEvaluating the efficacy of steganography techniques in the analysis of stego images encompasses a range of performance metrics. These metrics serve as valuable indicators for assessing the stego images' quality, security, and their ability to withstand detection efforts. While numerous metrics exist, our research focused specifically on the examination of PSNR, MSE, and SSIM as key indicators for our analysis of the stego images. Below are the formulas and steps to calculate the performance metrics PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Square Error), and SSIM (Structural Similarity Index) for analysing stego images:\u003c/p\u003e \u003cp\u003ea. Peak Signal-to-Noise Ratio (PSNR): PSNR measures the quality of a reconstructed or compressed signal concerning the original signal. It can be represented as:\u003c/p\u003e \u003cp\u003ePSNR (dB)\u0026thinsp;=\u0026thinsp;10 * log10((MAX^2) / MSE)\u003c/p\u003e \u003cp\u003eIn this equation:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMAX denotes the maximum attainable pixel value (e.g., 255 for an 8-bit image).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMSE stands for the Mean Squared Error.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eb. Mean Squared Error (MSE): MSE calculates the average squared difference between the original and reconstructed signals or images. It can be computed as:\u003c/p\u003e \u003cp\u003eMSE = (1 / (row * col)) * Σ [ Σ (P_original - P_reconstructed) ^2]\u003c/p\u003e \u003cp\u003eIn this equation:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003erow and col represent the dimensions of the image.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eP_original refers to the pixel value in the original image.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eP_reconstructed corresponds to the pixel value in the reconstructed image.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003ec. Structural Similarity Index (SSIM): SSIM is a metric that quantifies the structural similarity between two images, considering luminance, contrast, and structure. It can be expressed as:\u003c/p\u003e \u003cp\u003eSSIM (a, b) = [(2 * \u0026micro;_a * \u0026micro;_b\u0026thinsp;+\u0026thinsp;k1) * (2 * σ_ab\u0026thinsp;+\u0026thinsp;k2)] / [(\u0026micro;_a^2\u0026thinsp;+\u0026thinsp;\u0026micro;_b^2\u0026thinsp;+\u0026thinsp;k1) * (σ_a^2\u0026thinsp;+\u0026thinsp;σ_b^2\u0026thinsp;+\u0026thinsp;k2)]\u003c/p\u003e \u003cp\u003eIn this equation:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ea and b denote the two images being compared.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026micro;_a and \u0026micro;_b represents the means of a and b, respectively.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eσ_a and σ_b signifies the standard deviations of a and b, respectively.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eσ_ab stands for the covariance of a and b.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ek1 and k2 are small constants included to avoid division by zero and ensure the stability of the formula.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe performance metrics are computed by comparing the original secret image with the output generated by the Decompressor module, as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the study conducted by De Rosal Igantius Moses Setiadi et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], they reported PSNR values of 51.1449 after LSB (Least Significant Bit) and 36.0354 after PVD (Pixel Value Differencing) calculations. In their paper [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], a PSNR value of 65.5 was specifically noted for the Lena image. In our own research, we achieved a maximum PSNR value of 81.31 when comparing the secret image with the decompressed image, using a payload of 79,747 bytes. Additionally, the SSIM (Structural Similarity Index) achieved a perfect score of 1.0 for the same payload.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion and Future Enhancement","content":"\u003cp\u003eThe proposed approach employs a unique framework called the Mimic Octopus Adaptive Framework (MOAF), which is designed to enhance the intricacy of the steganalysis process. This objective is accomplished by introducing variation in compression and encryption outcomes, with the entropy of the image acting as the triggering factor. In this research, the MOAF (Mimic Octopus Adaptive Framework) framework has been effectively employed, starting with Shannon entropy-based entropy calculation to understand image information content. Lossless compression using Transform coding achieved a Compression Ratio (CR) of 1.16 and required minimal Processing Time (PT), preserving perceptual quality while reducing image size. Huffman coding further reduced file size in the lossy compression phase. Robust image security was ensured through encryption with 3DES and AES, yielding an impressive Peak Signal-to-Noise Ratio (PSNR) of 100 dB when comparing the decrypted image with the original. Additionally, LSB Steganography in the Encoder module demonstrated a maximum PSNR of 81.31, indicating the ability to embed data with minimal perceptual distortion. These findings collectively underscore the MOAF framework's capacity to balance compression efficiency, data security, and perceptual quality, making it a promising solution for medical image processing and secure transmission while maintaining efficient processing times.\u003c/p\u003e \u003cp\u003eIn summary, taking into account all the implementations and results discussed, it becomes evident that while steganalysis has the potential to detect the presence of a concealed image within a cover image, our optimization technique has notably increased the complexity involved in recovering the secret image. This heightened complexity results from the utilization of a diverse set of compression and encryption algorithms within our approach. Our future plans entail integrating additional encryption algorithms into the ensemble mimic octopus encryption framework. The objective is to generate a greater diversity of outcomes, thereby increasing the complexity of steganalysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eACKNOWLEDGEMENT\u003c/p\u003e\n\u003cp\u003eEthical Approval: The dataset used in this study comprises de-identified medical ultrasound images and was obtained from a publicly available source on Kaggle: https://www.kaggle.com/datasets/pallavisantha/pcod-ultrasound-images. No administrative permissions were required to access or use the dataset. As this study did not involve direct interaction with human participants or the collection of new human data, approval from an institutional ethics committee was not required. According to the information provided by the original dataset creators, all experimental protocols were approved by the relevant institutional review board, and informed consent was obtained from all participants, including from parents or legally authorized representatives for subjects under the age of 16. All data usage adhered to relevant ethical guidelines and regulations, including those outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eAuthor Contribution: Judy S conceptualized the research idea, developed the methodology, performed the experiments, and carried out the data analysis. She also drafted the initial manuscript. Dr. Rashmita Khilar provided guidance throughout the research process, supervised the project, contributed to refining the methodology, and critically reviewed and edited the manuscript. Both authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003eConsent to Participate: Not applicable for this section\u003c/p\u003e\n\u003cp\u003eConsent to Publish: Not applicable for this section\u003c/p\u003e\n\u003cp\u003eConflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eFunding:\u0026nbsp;There is no funding available.\u003c/p\u003e\n\u003cp\u003eDATA AVAILABILITY\u003c/p\u003e\n\u003cp\u003eThe dataset utilized in this study was sourced from the publicly accessible Kaggle repository, specifically from the PCOD Ultrasound Images dataset curated by Pallavi Santha. It can be accessed at the following URL: https://www.kaggle.com/datasets/pallavisantha /pcod-ultrasound-images..\u003c/p\u003e\n\u003cp\u003ePUBLISHER’S NOTE\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editor and the reviewers. Any statements, claims, performances and results are not guaranteed or endorsed by the publisher.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShoaib Amin Banday, Ajaz Hussain Mir, and Shakeel Ahmad Malik, \u0026ldquo;Multilevel medical image encryption for secure communication,\u0026rdquo; \u003cem\u003eElsevier eBooks\u003c/em\u003e, pp. 233\u0026ndash;252, Jan. 2020, doi: https://doi.org/10.1016/b978-0-12-820024-7.00012-8.\u003c/li\u003e\n\u003cli\u003eB. Gupta, M. Tiwari, and S. 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Shashidhara, and K. N. Balasubramanya Murthy, \u0026ldquo;Image Compression Using SVD,\u0026rdquo; \u003cem\u003eIEEE Xplore\u003c/em\u003e, Dec. 01, 2007. https://ieeexplore.ieee.org/ document/4426357 (accessed May 24, 2022).\u003c/li\u003e\n\u003cli\u003eBilal Bataineh, \u0026ldquo;Image contrast enhancement for preserving entropy and image visual features,\u0026rdquo; \u003cem\u003eInternational Journal of Advances in Intelligent Informatics\u003c/em\u003e, vol. 9, no. 2, pp. 161\u0026ndash;161, Jul. 2023, doi: https://doi.org/10.26555/ijain.v9i2.907.\u003c/li\u003e\n\u003cli\u003eD. He and S. Xiong, \u0026ldquo;Image Processing Design and Algorithm Research Based on Cloud Computing,\u0026rdquo; \u003cem\u003eJournal of Sensors\u003c/em\u003e, vol. 2021, p. e9198884, Oct. 2021, doi: https://doi.org/10.1155/2021/9198884.\u003c/li\u003e\n\u003cli\u003eE. Z. Astuti, M. Setiadi, Eko Hari Rachmawanto, Christy Atika Sari, and K. Sarker, \u0026ldquo;LSB-based Bit Flipping Methods for Color Image Steganography,\u0026rdquo; vol. 1501, no. 1, pp. 012019\u0026ndash;012019, Mar. 2020, doi: https://doi.org/10.1088/1742-6596/1501/1/012019.\u003c/li\u003e\n\u003cli\u003eS. Alsamaraee and A. S. Ali, \u0026ldquo;A crypto-steganography scheme for IoT applications based on bit interchange and crypto-system,\u0026rdquo; \u003cem\u003eBulletin of Electrical Engineering and Informatics\u003c/em\u003e, vol. 11, no. 6, pp. 3539\u0026ndash;3550, Dec. 2022, doi: https://doi.org/10.11591/eei.v11i6.4194.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Image Compression, Image Pre-processing, Steganography, Cryptography, Image Security, Mimic Octopus Adaptive Framework","lastPublishedDoi":"10.21203/rs.3.rs-6300749/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6300749/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the realm of secure communication, invisible steganography plays a crucial role by allowing discreet data transmission within digital images while preserving their visual integrity. This study introduces a pioneering method aimed at enhancing the security and concealment of steganographic data. It achieves this by utilizing entropy-based image encryption in combination with a novel Mimic Octopus Adaptive Framework (MOAF). The primary objective of MOAF is to establish a higher level of image security, denoted as the third tier, by dynamically selecting suitable compression and encryption algorithms based on the entropy characteristics of the secret image. Our approach attained an impressive peak signal-to-noise ratio (PSNR) of 81.31 when comparing the secret image to the decompressed image, with a payload size of 79,747 bytes. Additionally, the structural similarity index (SSIM) achieved a perfect score of 1.0 under the same payload conditions. Comprehensive experiments and comparative analyses against established steganographic techniques demonstrate that our proposed system excels in security, imperceptibility, and resilience against various attacks. These findings underscore the potential of entropy-based image encryption, combined with the innovative Mimic Octopus Adaptive Framework (MOAF), to advance invisible steganography and enable more secure and clandestine data transmission in the digital age.\u003c/p\u003e","manuscriptTitle":"Adaptive Invisible Steganography Leveraging Entropy-Based Image Encryption with the Mimic Octopus Adaptive Framework (MOAF)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 14:48:19","doi":"10.21203/rs.3.rs-6300749/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-19T16:58:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-14T08:30:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-13T09:05:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249611352872480140275996874800920267313","date":"2025-05-10T13:15:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319412907133577196759944953654739719783","date":"2025-05-06T09:06:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-06T05:41:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131390369807022455496561399297080504827","date":"2025-05-06T05:38:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65268932712469633376276277602952357993","date":"2025-05-06T04:23:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-05T07:57:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178774386425364368524588423319245467783","date":"2025-05-02T10:52:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-01T15:39:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78592773035531059858478704514953463256","date":"2025-05-01T15:33:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-01T06:27:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97801221699832301832280572453476504150","date":"2025-05-01T06:11:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101712293797589657896317043705223916497","date":"2025-05-01T05:58:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-01T05:02:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-01T04:54:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-29T09:02:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-24T10:26:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Computing","date":"2025-04-24T10:25:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d6338567-3a1d-4f8f-ad21-b36a45164b1a","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:01:57+00:00","versionOfRecord":{"articleIdentity":"rs-6300749","link":"https://doi.org/10.1007/s10791-025-09719-7","journal":{"identity":"discover-computing","isVorOnly":false,"title":"Discover Computing"},"publishedOn":"2025-10-07 15:58:02","publishedOnDateReadable":"October 7th, 2025"},"versionCreatedAt":"2025-05-08 14:48:19","video":"","vorDoi":"10.1007/s10791-025-09719-7","vorDoiUrl":"https://doi.org/10.1007/s10791-025-09719-7","workflowStages":[]},"version":"v1","identity":"rs-6300749","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6300749","identity":"rs-6300749","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}