Classification of Diverse AI Generated Content: An In-Depth Exploration using Machine Learning and Knowledge Graphs

As the use of AI-generated content grows, it becomes imperative to recognize which data is human-developed and which is not. AI-generated content comes in several modalities such as deepfakes, text and images. This article proposes techniques that can be used to tackle this problem. To identify deepfakes we can take advantage of characteristics that make us human: blood circulation, human emotions and winks among other things. Text recognition takes a different approach, using a combination of large-scale language models, style analysis, and visual analysis. The generated images can be recognized by analyzing the pixels and common colors and patterns. It can be concluded that AI-generated content is the future, and it is difficult to develop a model that can identify it with 100\% accuracy. Validating content before submitting it and using it responsibly are the steps we should take to adapt to a world where both types of content coexist.