159 citations · 224 across the 9 of their papers we have counts for
4 papers · 1 filter
Real-world Adversarial Defense against Patch Attacks based on Diffusion Model
Xingxing Wei, Caixin Kang, Yinpeng Dong +4
Adversarial patches present significant challenges to the robustness of deep learning models, making the development of effective defenses become critical for real-world applicatio…
DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks
Caixin Kang, Yinpeng Dong, Zhengyi Wang +4
Adversarial attacks, particularly patch attacks, pose significant threats to the robustness and reliability of deep learning models. Developing reliable defenses against patch atta…
Robust Classification via a Single Diffusion Model
Huanran Chen, Yinpeng Dong, Zhengyi Wang +4
Diffusion models have been applied to improve adversarial robustness of image classifiers by purifying the adversarial noises or generating realistic data for adversarial training.…
PREIM3D: 3D Consistent Precise Image Attribute Editing from a Single Image
Jianhui Li, Jianmin Li, Haoji Zhang +5
We study the 3D-aware image attribute editing problem in this paper, which has wide applications in practice. Recent methods solved the problem by training a shared encoder to map…