Robust manipulated media localization and detection based on high frequency and texture features

Advances in facial manipulation techniques have resulted in the increasing trend of realistic and indistinguishable identity swap media, which mislead the viewers and accompanied by severe security concerns. While current deepfake detectors demonstrate strong performance under high-quality conditions, they still face notable limitations. This article proposes a novel framework mining high frequency and degraded texture features for locating manipulated traces and improving the generalization ability. To improve the universality of the proposed detector, we design the Multi-feature Mining Stream for capturing the global and subtle discrepancies of undegraded images. Moreover, the Encoder-Decoder Structure is introduced for gaining high localization accuracy and full resolution manipulated regions. This work attempts to solve the tampered region localization issue and achieve face forgery image detection at the meantime. This contributes to help the model perform a more effective differentiation between real and fake content when confronted with high- or low-quality compressed images. Comprehensive experiments on the popular FaceForensics++, Celeb-DF, and DFDC datasets demonstrate the superior performance and robustness of our proposed framework, in particular, achieving performance improvements ranging from 1% to 10% in comparison with the most recent related work.