1 citations · 3 across the 17 of their papers we have counts for
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Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations
Decheng Liu, Zongqi Wang, Chunlei Peng +3
Due to the successful development of deep image generation technology, forgery detection plays a more important role in social and economic security. Racial bias has not been explo…
Federated Face Forgery Detection Learning with Personalized Representation
Decheng Liu, Zhan Dang, Chunlei Peng +3
Deep generator technology can produce high-quality fake videos that are indistinguishable, posing a serious social threat. Traditional forgery detection methods directly centralize…
Improving Adversarial Robustness via Decoupled Visual Representation Masking
Decheng Liu, Tao Chen, Chunlei Peng +3
Deep neural networks are proven to be vulnerable to fine-designed adversarial examples, and adversarial defense algorithms draw more and more attention nowadays. Pre-processing bas…
Imperceptible Face Forgery Attack via Adversarial Semantic Mask
Decheng Liu, Qixuan Su, Chunlei Peng +2
With the great development of generative model techniques, face forgery detection draws more and more attention in the related field. Researchers find that existing face forgery mo…
AFD: Mitigating Feature Gap for Adversarial Robustness by Feature Disentanglement
Nuoyan Zhou, Dawei Zhou, Decheng Liu +2
Adversarial fine-tuning methods enhance adversarial robustness via fine-tuning the pre-trained model in an adversarial training manner. However, we identify that some specific late…
Masked Attribute Description Embedding for Cloth-Changing Person Re-identification
Chunlei Peng, Boyu Wang, Decheng Liu +3
Cloth-changing person re-identification (CC-ReID) aims to match persons who change clothes over long periods. The key challenge in CC-ReID is to extract clothing-independent featur…