Locate and Verify: A Two-Stream Network for Improved Deepfake Detection

• URL: http://arxiv.org/abs/2309.11131v1
• Date: Wed, 20 Sep 2023 08:25:19 GMT
• Title: Locate and Verify: A Two-Stream Network for Improved Deepfake Detection
• Authors: Chao Shuai, Jieming Zhong, Shuang Wu, Feng Lin, Zhibo Wang, Zhongjie Ba, Zhenguang Liu, Lorenzo Cavallaro, Kui Ren
• Abstract summary: Current deepfake detection methods are typically inadequate in generalizability. We propose an innovative two-stream network that effectively enlarges the potential regions from which the model extracts evidence. We also propose a Semi-supervised Patch Similarity Learning strategy to estimate patch-level forged location annotations.
• Score: 33.50963446256726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deepfake has taken the world by storm, triggering a trust crisis. Current deepfake detection methods are typically inadequate in generalizability, with a tendency to overfit to image contents such as the background, which are frequently occurring but relatively unimportant in the training dataset. Furthermore, current methods heavily rely on a few dominant forgery regions and may ignore other equally important regions, leading to inadequate uncovering of forgery cues. In this paper, we strive to address these shortcomings from three aspects: (1) We propose an innovative two-stream network that effectively enlarges the potential regions from which the model extracts forgery evidence. (2) We devise three functional modules to handle the multi-stream and multi-scale features in a collaborative learning scheme. (3) Confronted with the challenge of obtaining forgery annotations, we propose a Semi-supervised Patch Similarity Learning strategy to estimate patch-level forged location annotations. Empirically, our method demonstrates significantly improved robustness and generalizability, outperforming previous methods on six benchmarks, and improving the frame-level AUC on Deepfake Detection Challenge preview dataset from 0.797 to 0.835 and video-level AUC on CelebDF$\_$v1 dataset from 0.811 to 0.847. Our implementation is available at https://github.com/sccsok/Locate-and-Verify.

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