2 citations · 4 across the 5 of their papers we have counts for
5 papers
A Survey of Robustness and Safety of 2D and 3D Deep Learning Models Against Adversarial Attacks
Yanjie Li, Bin Xie, Songtao Guo +2
Benefiting from the rapid development of deep learning, 2D and 3D computer vision applications are deployed in many safe-critical systems, such as autopilot and identity authentica…
Generating Transferable and Stealthy Adversarial Patch via Attention-guided Adversarial Inpainting
Yanjie Li, Mingxing Duan, Xuelong Dai +1
Adversarial patch attacks can fool the face recognition (FR) models via small patches. However, previous adversarial patch attacks often result in unnatural patterns that are easil…
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models
Xuelong Dai, Kaisheng Liang, Bin Xiao
Unrestricted adversarial attacks present a serious threat to deep learning models and adversarial defense techniques. They pose severe security problems for deep learning applicati…
StyLess: Boosting the Transferability of Adversarial Examples
Kaisheng Liang, Bin Xiao
Adversarial attacks can mislead deep neural networks (DNNs) by adding imperceptible perturbations to benign examples. The attack transferability enables adversarial examples to att…
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…