A Dissimilarity-Based Countermeasure for Detecting Replay Attacks in Speaker Verification

Audio replay attacks present a significant challenge to automatic speaker verification systems (ASVs), emphasizing the need for effective detection methods. Traditionally, embedding-based approaches, such as those leveraging Convolutional Neural Networks (CNNs), have been used. However, dissimilarity-based methods emerge as a promising alternative, offering potential advantages in detecting subtle differences between genuine and spoofed audio. This study evaluates dissimilarity strategies for detecting genuine versus spoofed audio signals using a well-known benchmark dataset and established metrics, including accuracy and Equal Error Rate (EER). We provide a comparative performance assessment of various CNN architectures and dissimilarity strategies, finding that while dissimilarity approaches are competitive with embedding-based methods, the Dissimilarity Vectors strategy outperforms the Dissimilarity Space strategy.