Multi-Scale Feature Enhancement Network for Face Forgery Detection

Nowadays, synthesizing realistic fake face images and videos becomes easy benefiting from the advance in generation technology. With the popularity of face forgery, abuse of the technology occurs from time to time, which promotes the research on face forgery detection to be an emergency. To deal with the potential risks, we propose a face forgery detection method based on multi-scale feature enhancement. Specifically, we analyze the forgery traces from the perspective of texture and frequency domain, respectively. We find that forgery traces are hard to be perceived by human eyes but noticeable in shallow layers of CNNs and middle-frequency domain and high-frequency domain. Hence, to reserve more forgery information, we design a texture feature enhancement module and a frequency domain feature enhancement module, respectively. The experiments on FaceForensics++ dataset and Celeb-DF dataset show that our method exceeds most existing networks and methods, which proves that our method has strong classification ability.