Dual-Tree Complex Wavelet Transform-Based Direction Correlation for Face Forgery Detection

With the rapid development of face synthesis techniques, things are going from bad to worse as high-quality fake face images are unnoticeable by human eyes, which has brought serious public confidence and security problems. Thus, effective detection of face image forgeries is in urgent need. We observe that some subtle artificial artifacts in spatial domain can be easily recognized in transformation domain, and most facial features have an inherent directional correlation, and generative models would ruffle this kind of distribution pattern. Inspired by this, we propose a two-stream dual-tree complex wavelet-based face forgery network (DCWNet) to expose face image forgeries. Specifically, dual-tree complex wavelet transform is exploited to obtain six directional features (±75°, ±45°, ±15°) of different frequency components from original images, and a direction correlation extraction (DCE) block is presented to capture the direction correlation. Then, the direction pattern-aware clues and the original image are taken as two complementary network inputs. We also explore how specific frequency components work in face forgery detection and propose a new multiscale channel attention mechanism for features fusion. The experimental results prove that the proposed DCWNet outperforms the state-of-the-art methods in open datasets such as FaceForensics++ and achieves high robustness against lossy image compression.