Local Corner and Motion Key Point Trajectory Extraction for Facial Forgery Identification

Abstract

At present, the development of deep forgery technology has brought new challenges to media content forensics, and the use of deep forgery identification methods to identify forged audio and video has become a significant focus of research and difficulty. Deep forgery technology and forensic technology play a mutual game and promote each other’s development. This paper proposes a spatiotemporal local feature abstraction (STLFA) framework for facial forgery identification to solve the media industry challenges of deep forgery technology. To adequately utilize local facial features, we combine facial key points, key point movement, and facial corner points to detect forgery content. This paper establishes a spatiotemporal relation, which realizes face forgery detection by identifying abnormalities of facial keypoints and corner points for interframe judgments. Meanwhile, we utilize RNNs to predict the sequences from facial key point movement abnormalities and corner points for interframe. Experimental results show that our method achieves better performance than some existing methods and good anticompression forgery face detection performance on FF++.