  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.
