Metric Learning for Anti-Compression Facial Forgery Detection

Detecting facial forgery images and videos is an increasingly important topic in multimedia forensics. As forgery images and videos are usually compressed to different formats such as JPEG and H264 when circulating on the Internet, existing forgery-detection methods trained on uncompressed data often have significantly decreased performance in identifying them. To solve this problem, we propose a novel anti-compression facial forgery detection framework, which learns a compression-insensitive embedding feature space utilizing both original and compressed forgeries. Specifically, our approach consists of two novel ideas: (i) extracting compression-insensitive features from both uncompressed and compressed forgeries using an adversarial learning strategy; (ii) learning a robust partition by constructing a metric loss that can reduce the distance of the paired original and compressed images in the embedding space. Experimental results demonstrate that, the proposed method is highly effective in handling both compressed and uncompressed facial forgery images.