Self-consistency and language guidance for generalizable deepfake detection.
Two complementary approaches to detecting synthetic faces, designed for the deployment problem rather than the leaderboard problem.
Pair-wise self-consistency learning(Zhao et al., 2021; Zhao et al., 2020) (ICCV 2021 oral) trains a model to spot the source-feature inconsistency that GenAI generators leave behind, paired with an inconsistency image generator that synthesizes richly annotated training data. The hypothesis worth testing: even when a generator’s output is photorealistic to a human, the source features within the forged image preserve a fingerprint detectable by ConvNets.
AuthGuard(Shen et al., 2026) (WACV 2026) extends the same instinct with a language-guided expert encoder that reasons about logical and perceptual anomalies the way a person would, on top of the statistical artifacts. AUC gains of 6.15% on DFDC and 16.68% on DF40, with a 24.69% improvement on the DDVQA reasoning benchmark.
These ideas shaped the omni-modal trust work and the production model-diagnosis program that addresses the failure modes uncovered by deepfake detection at production scale.
@inproceedings{shen2026authguard,title={AuthGuard: Generalizable Deepfake Detection via Language Guidance},author={Shen, Guangyu and Li, Zhihua and Xu, Xiang and Zhao, Tianchen and Zhang, Zheng and An, Dongsheng and Tu, Zhuowen and Xing, Yifan and Zhang, Qin},booktitle={IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},year={2026},}
@inproceedings{zhao2021selfconsistency,title={Learning Self-Consistency for Deepfake Detection},author={Zhao, Tianchen and Xu, Xiang and Xu, Mingze and Ding, Hui and Xiong, Yuanjun and Xia, Wei},booktitle={IEEE/CVF International Conference on Computer Vision (ICCV)},year={2021},note={Oral}}
2020
arXiv
Learning to Recognize Patch-Wise Consistency for Deepfake Detection
Tianchen Zhao, Xiang Xu, Mingze Xu, and 3 more authors
@article{zhao2020learning,title={Learning to Recognize Patch-Wise Consistency for Deepfake Detection},author={Zhao, Tianchen and Xu, Xiang and Xu, Mingze and Ding, Hui and Xiong, Yuanjun and Xia, Wei},journal={arXiv preprint arXiv:2012.09311},year={2020},}