Unlocking A New Paradigm In Robustness For Multi-Step Facial Forgery Detection

With the rapid advancement of face forgery technologies, the quality of manipulated images has significantly improved, posing a severe threat to information security. In response, deepfake detection has emerged as an effective countermeasure against the misuse of these technologies. Sequential deepfake detection,as a specialized extension, targets face images with multi-step manipulation. However, a key challenge in this task is defending against unknown image degradation that occurs during transformation, which is not widely addressed in previous research. This paper introduces a robust detection framework named RSFDF, aimed at enhancing detection capabilities when images are subjected to degradation operations. RSFDF incorporates two critical modules:ATEM and ESCM. ATEM assists the network in focusing on important features while suppressing irrelevant information; ESCM refines the attention mechanism to increase the model’s focus on edge contours, aiding in the judgment of sequential forgeries. Experiments show that RSFDF exhibits significant improvements in robustness against unknown image degradations.