15 citations · 21 across the 3 of their papers we have counts for
7 papers
Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks
Shengming Yuan, Qilong Zhang, Lianli Gao +2
Unrestricted color attacks, which manipulate semantically meaningful color of an image, have shown their stealthiness and success in fooling both human eyes and deep neural network…
Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack
Ye Liu, Yaya Cheng, Lianli Gao +3
Defense models against adversarial attacks have grown significantly, but the lack of practical evaluation methods has hindered progress. Evaluation can be defined as looking for de…
Adversarial Attacks on ML Defense Models Competition
Yinpeng Dong, Qi-An Fu, Xiao Yang +25
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.…
Feature Space Targeted Attacks by Statistic Alignment
Lianli Gao, Yaya Cheng, Qilong Zhang +2
By adding human-imperceptible perturbations to images, DNNs can be easily fooled. As one of the mainstream methods, feature space targeted attacks perturb images by modulating thei…
Patch-wise++ Perturbation for Adversarial Targeted Attacks
Lianli Gao, Qilong Zhang, Jingkuan Song +1
Although great progress has been made on adversarial attacks for deep neural networks (DNNs), their transferability is still unsatisfactory, especially for targeted attacks. There…
Patch-wise Attack for Fooling Deep Neural Network
Lianli Gao, Qilong Zhang, Jingkuan Song +2
By adding human-imperceptible noise to clean images, the resultant adversarial examples can fool other unknown models. Features of a pixel extracted by deep neural networks (DNNs)…