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cs.CV2025★ 1 cited
CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation
Yuxuan Du, Zhendong Wang, Yuhao Luo +4
The rapid emergence of multimodal deepfakes (visual and auditory content are manipulated in concert) undermines the reliability of existing detectors that rely solely on modality-s…
cs.CV2024★ 1 cited
Can We Leave Deepfake Data Behind in Training Deepfake Detector?
Jikang Cheng, Zhiyuan Yan, Ying Zhang +3
The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with…
cs.CV2023
Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection
Zhiyuan Yan, Yuhao Luo, Siwei Lyu +2
Deepfake detection faces a critical generalization hurdle, with performance deteriorating when there is a mismatch between the distributions of training and testing data. A broadly…