Metric Learning for Anti-Compression Facial Forgery Detection
This addresses performance degradation in forgery detection for compressed multimedia, which is crucial for multimedia forensics, but it is incremental as it builds on existing detection methods.
The paper tackles the problem of detecting facial forgeries that degrade under compression by proposing a framework that learns compression-insensitive features using adversarial learning and metric loss, achieving high effectiveness in handling both compressed and uncompressed images.
Detecting facial forgery images and videos is an increasingly important topic in multimedia forensics. As forgery images and videos are usually compressed into different formats such as JPEG and H264 when circulating on the Internet, existing forgery-detection methods trained on uncompressed data often suffer from significant performance degradation 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 three 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; (iii) improving the accuracy of tampered localization with an attention-transfer module. Experimental results demonstrate that, the proposed method is highly effective in handling both compressed and uncompressed facial forgery images.