5 papers
A Knowledge-guided Adversarial Defense for Resisting Malicious Visual Manipulation
Dawei Zhou, Suzhi Gang, Decheng Liu +3
Malicious applications of visual manipulation have raised serious threats to the security and reputation of users in many fields. To alleviate these issues, adversarial noise-based…
Disentangle Before Anonymize: A Two-stage Framework for Attribute-preserved and Occlusion-robust De-identification
Mingrui Zhu, Dongxin Chen, Xin Wei +2
In an era where personal photos are easily leaked and collected, face de-identification is a crucial method for protecting identity privacy. However, current face de-identification…
iFADIT: Invertible Face Anonymization via Disentangled Identity Transform
Lin Yuan, Kai Liang, Xiong Li +3
Face anonymization aims to conceal the visual identity of a face to safeguard the individual's privacy. Traditional methods like blurring and pixelation can largely remove identify…
Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model
Jiahua Xu, Dawei Zhou, Lei Hu +5
Motion artifacts present in magnetic resonance imaging (MRI) can seriously interfere with clinical diagnosis. Removing motion artifacts is a straightforward solution and has been e…
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…