18 citations · 68 across the 12 of their papers we have counts for
12 papers
Boosting Adversarial Attacks by Leveraging Decision Boundary Information
Boheng Zeng, LianLi Gao, QiLong Zhang +3
Due to the gap between a substitute model and a victim model, the gradient-based noise generated from a substitute model may have low transferability for a victim model since their…
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…
Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformation
Qilong Zhang, Youheng Sun, Chaoning Zhang +4
In recent years, the adversarial vulnerability of deep neural networks (DNNs) has raised increasing attention. Among all the threat models, no-box attacks are the most practical bu…
Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains
Qilong Zhang, Xiaodan Li, Yuefeng Chen +4
Adversarial examples have posed a severe threat to deep neural networks due to their transferable nature. Currently, various works have paid great efforts to enhance the cross-mode…
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.…
Fast Gradient Non-sign Methods
Yaya Cheng, Jingkuan Song, Xiaosu Zhu +3
Adversarial attacks make their success in DNNs, and among them, gradient-based algorithms become one of the mainstreams. Based on the linearity hypothesis, under cons…
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…
Staircase Sign Method for Boosting Adversarial Attacks
Qilong Zhang, Xiaosu Zhu, Jingkuan Song +2
Crafting adversarial examples for the transfer-based attack is challenging and remains a research hot spot. Currently, such attack methods are based on the hypothesis that the subs…
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)…
Understanding the Temporal Fading in Wireless Industrial Networks: Measurements and Analyses
Qilong Zhang, Qiwei Zhang, Wuxiong Zhang +3
The wide deployment of wireless industrial networks still faces the challenge of unreliable service due to severe multipath fading in industrial environments. Such fading effects a…