5 citations · 8 across the 4 of their papers we have counts for
4 papers
Enhancing Clean Label Backdoor Attack with Two-phase Specific Triggers
Nan Luo, Yuanzhang Li, Yajie Wang +3
Backdoor attacks threaten Deep Neural Networks (DNNs). Towards stealthiness, researchers propose clean-label backdoor attacks, which require the adversaries not to alter the labels…
Improving the Transferability of Adversarial Examples with Restructure Embedded Patches
Huipeng Zhou, Yu-an Tan, Yajie Wang +3
Vision transformers (ViTs) have demonstrated impressive performance in various computer vision tasks. However, the adversarial examples generated by ViTs are challenging to transfe…
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?
Yonggan Fu, Shunyao Zhang, Shang Wu +2
Vision transformers (ViTs) have recently set off a new wave in neural architecture design thanks to their record-breaking performance in various vision tasks. In parallel, to fulfi…
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks
Yonggan Fu, Qixuan Yu, Yang Zhang +4
Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks, i.e., an imperceptible perturbation to the input can mislead DNNs trained on clean images into making…