2 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
Gradient-Free Adversarial Purification with Diffusion Models
Xuelong Dai, Dong Wang, Xiuzhen Cheng +1
Adversarial training and adversarial purification are two widely used defense strategies for enhancing model robustness against adversarial attacks. However, adversarial training r…
Improving Transferable Targeted Attacks with Feature Tuning Mixup
Kaisheng Liang, Xuelong Dai, Yanjie Li +2
Deep neural networks (DNNs) exhibit vulnerability to adversarial examples that can transfer across different DNN models. A particularly challenging problem is developing transferab…
Transferable 3D Adversarial Shape Completion using Diffusion Models
Xuelong Dai, Bin Xiao
Recent studies that incorporate geometric features and transformers into 3D point cloud feature learning have significantly improved the performance of 3D deep-learning models. How…
Physical-World Optical Adversarial Attacks on 3D Face Recognition
Yanjie Li, Yiquan Li, Xuelong Dai +2
2D face recognition has been proven insecure for physical adversarial attacks. However, few studies have investigated the possibility of attacking real-world 3D face recognition sy…
Generating Unrestricted 3D Adversarial Point Clouds
Xuelong Dai, Yanjie Li, Hua Dai +1
Utilizing 3D point cloud data has become an urgent need for the deployment of artificial intelligence in many areas like facial recognition and self-driving. However, deep learning…