Learning Self-Consistency for Deepfake Detection

Tianchen Zhao, Xiang Xu, Mingze Xu, Hui Ding, Yuanjun Xiong, Wei Xia; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15023-15033


We propose a new method to detect deepfake images using the cue of the source feature inconsistency within the forged images. It is based on the hypothesis that images' distinct source features can be preserved and extracted after going through state-of-the-art deepfake generation processes. We introduce a novel representation learning approach, called pair-wise self-consistency learning (PCL), for training ConvNets to extract these source features and detect deepfake images. It is accompanied by a new image synthesis approach, called inconsistency image generator (I2G), to provide richly annotated training data for PCL. Experimental results on seven popular datasets show that our models improve averaged AUC from 96.45% to 98.05% over the state of the art in the in-dataset evaluation and from 86.03% to 92.18% in the cross-dataset evaluation.

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[pdf] [supp] [arXiv]
@InProceedings{Zhao_2021_ICCV, author = {Zhao, Tianchen and Xu, Xiang and Xu, Mingze and Ding, Hui and Xiong, Yuanjun and Xia, Wei}, title = {Learning Self-Consistency for Deepfake Detection}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {15023-15033} }