Affiliation:
1. University of Science and Technology of China
2. Kuaishou Technology
Abstract
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.
Publisher
International Joint Conferences on Artificial Intelligence Organization
Cited by
15 articles.
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1. Adaptive Texture and Spectrum Clue Mining for Generalizable Face Forgery Detection;IEEE Transactions on Information Forensics and Security;2024
2. MSVT: Multiple Spatiotemporal Views Transformer for DeepFake Video Detection;IEEE Transactions on Circuits and Systems for Video Technology;2023-09
3. Exploiting Complementary Dynamic Incoherence for DeepFake Video Detection;IEEE Transactions on Circuits and Systems for Video Technology;2023-08
4. Self-Supervised Video Forensics by Audio-Visual Anomaly Detection;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06
5. TI2Net: Temporal Identity Inconsistency Network for Deepfake Detection;2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2023-01