187 citations · 380 across the 41 of their papers we have counts for
7 papers · 1 filter
Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks
Xingxing Wei, Ying Guo, Jie Yu +1
Adversarial patch is an important form of real-world adversarial attack that brings serious risks to the robustness of deep neural networks. Previous methods generate adversarial p…
Visual Adversarial Attacks and Defenses in the Physical World: A Survey
Xingxing Wei, Bangzheng Pu, Shiji Zhao +2
Although Deep Neural Networks (DNNs) have been widely applied in various real-world scenarios, they remain vulnerable to adversarial examples. Adversarial attacks in computer visio…
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints
Yinpeng Dong, Shouwei Ruan, Hang Su +3
Recent studies have demonstrated that visual recognition models lack robustness to distribution shift. However, current work mainly considers model robustness to 2D image transform…
Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection
Maoxun Yuan, Yinyan Wang, Xingxing Wei
Integrating multispectral data in object detection, especially visible and infrared images, has received great attention in recent years. Since visible (RGB) and infrared (IR) imag…
Prior-Guided Adversarial Initialization for Fast Adversarial Training
Xiaojun Jia, Yong Zhang, Xingxing Wei +4
Fast adversarial training (FAT) effectively improves the efficiency of standard adversarial training (SAT). However, initial FAT encounters catastrophic overfitting, i.e.,the robus…
Enhancing Transferability of Adversarial Examples with Spatial Momentum
Guoqiu Wang, Huanqian Yan, Xingxing Wei
Many adversarial attack methods achieve satisfactory attack success rates under the white-box setting, but they usually show poor transferability when attacking other DNN models. M…
Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection
Siyuan Liang, Baoyuan Wu, Yanbo Fan +2
Object detection has been widely used in many safety-critical tasks, such as autonomous driving. However, its vulnerability to adversarial examples has not been sufficiently studie…