Dynamic Inconsistency-aware DeepFake Video Detection
Dynamic Inconsistency-aware DeepFake Video Detection
Ziheng Hu, Hongtao Xie, YuXin Wang, Jiahong Li, Zhongyuan Wang, Yongdong Zhang
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Main Track. Pages 736-742.
https://doi.org/10.24963/ijcai.2021/102
The spread of DeepFake videos causes a serious threat to information security, calling for effective detection methods to distinguish them. However, the performance of recent frame-based detection methods become limited due to their ignorance of the inter-frame inconsistency of fake videos. In this paper, we propose a novel Dynamic Inconsistency-aware Network to handle the inconsistent problem, which uses a Cross-Reference module (CRM) to capture both the global and local inter-frame inconsistencies. The CRM contains two parallel branches. The first branch takes faces from adjacent frames as input, and calculates a structure similarity map for a global inconsistency representation. The second branch only focuses on the inter-frame variation of independent critical regions, which captures the local inconsistency. To the best of our knowledge, this is the first work to totally use the inter-frame inconsistency information from the global and local perspectives. Compared with existing methods, our model provides a more accurate and robust detection on FaceForensics++, DFDC-preview and Celeb-DFv2 datasets.
Keywords:
Computer Vision: Biometrics, Face and Gesture Recognition
Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation