22 citations · 49 across the 11 of their papers we have counts for
12 papers
Stability Analysis and Generalization Bounds of Adversarial Training
Jiancong Xiao, Yanbo Fan, Ruoyu Sun +2
In adversarial machine learning, deep neural networks can fit the adversarial examples on the training dataset but have poor generalization ability on the test set. This phenomenon…
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation
Zeyu Qin, Yanbo Fan, Yi Liu +4
Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples, which can produce erroneous predictions by injecting imperceptible perturbations. In this work…
Adaptive Smoothness-weighted Adversarial Training for Multiple Perturbations with Its Stability Analysis
Jiancong Xiao, Zeyu Qin, Yanbo Fan +3
Adversarial Training (AT) has been demonstrated as one of the most effective methods against adversarial examples. While most existing works focus on AT with a single type of pertu…
Understanding Adversarial Robustness Against On-manifold Adversarial Examples
Jiancong Xiao, Liusha Yang, Yanbo Fan +2
Deep neural networks (DNNs) are shown to be vulnerable to adversarial examples. A well-trained model can be easily attacked by adding small perturbations to the original data. One…
Improving the Latent Space of Image Style Transfer
Yunpeng Bai, Cairong Wang, Chun Yuan +2
Existing neural style transfer researches have studied to match statistical information between the deep features of content and style images, which were extracted by a pre-trained…
Sampling-based Fast Gradient Rescaling Method for Highly Transferable Adversarial Attacks
Xu Han, Anmin Liu, Yifeng Xiong +2
Deep neural networks have shown to be very vulnerable to adversarial examples crafted by adding human-imperceptible perturbations to benign inputs. After achieving impressive attac…