Face forgery detection via identification of evident tampered regions and multi-view analysis
Shuai Wang, Hanling Zhang, Gaobo Yang · Neurocomputing · 2025
In AI-synthesized faces, there usually exist prominent natural features, which poses a huge challenge for face forgery detection. In this work, we propose a Region-Aware Deep Neural Network (RDNN). RDNN calculates the tampering possibility of each face region based on the features learned from each region and selects the region with the highest tampering possibility as the detection result. Then, a new Latent Cue Capture Loss (LCCL) is designed to train RDNN to capture those fake face features ignored by traditional loss functions. Besides, by leveraging RDNN to locate forgeries, we propose a deepfake detection strategy namely RDNN-based Multi-Perspective Deepfake Detection (RMDD), to keep the advantages of RDNN while improving the detection robustness. Specifically, RMDD uses RDNN to locate suspected forgeries in the original and horizontally flipped faces, and mines local features in the vicinity of these suspected forgeries. Finally, the detection result is acquired by integrating the above detection clues. Ablation experiments verify the ability of RDNN to locate manipulated traces and the contribution of each component in RMDD. Moreover, experimental results demonstrate that RMDD has excellent detection accuracy and generalization ability.