Voice Spoofing Attacks and Countermeasures: A Systematic Review, Analysis, and Experimental Evaluation

Abstract With the advent of automated speaker verification (ASV) systems comes an equal and opposite development: malicious actors may seek to use voice spoofing attacks to fool those same systems. Various countermeasures have been proposed to detect these spoofing attacks, but current offerings in this arena fall short of a unified solution. For this reason, defensive measures for ASV systems produced in the last 6-7 years need to be classified, and qualitative and quantitative comparisons of state-of-the-art (SOTA) countermeasures should be performed to assess the effectiveness of these systems against real-world attacks. Hence, in this work, we conduct a review of the literature on spoof-ing detection using hand-crafted features, deep learning, and end-to-end spoofing countermeasure solutions to detect logical access (LA) attacks, such as speech synthesis (SS) and voice conversion (VC), and physical access (PA) attacks, i.e., replay attacks. Additionally, we review integrated and unified solutions to voice spoofing evaluation and speaker verification, and adversarial and anti-forensic attacks on both voice countermeasures and ASV systems. In an extensive experimental analysis, the limitations and challenges of existing spoofing countermeasures are presented, the performance of these countermeasures on several datasets is reported, and cross-corpus evaluations are performed, something that is nearly absent in the existing literature, in order to assess the gen-eralizability of existing solutions. For the experiments, we employ the 1 Springer Nature 2021 L A T E X template Voice Spoofing Attacks and Countermeasures ASVspoof2019, ASVspoof2021, and VSDC datasets along with GMM, SVM, CNN, and CNN-GRU classifiers. (For reproducibility of the results, the code of the testbed can be found at our GitHub Repository *).