Towards secure authentication: Detecting replay attacks via compression artifacts

This study presents a novel machine learning-based approach to mitigating digital replay attacks in face verification systems by leveraging compression artifacts to differentiate between compressed and uncompressed video frames. While traditional methods rely on active liveness detection, which can be inconvenient and negatively affect user experience, this work addresses the lack of automated solutions for detecting replay attacks. Using raw, uncompressed video datasets and widelyused video compression algorithms, the proposed method trains a classifier to identify compression artifacts as distinguishing features. Experimental results validate the model’s effectiveness in detecting injected content, highlighting the critical role of compression artifacts in enhancing the robustness of video authentication systems. This contribution represents a significant step toward advancing anti-spoofing techniques by exploring a previously underutilized aspect of video integrity.