30 citations · 100 across the 18 of their papers we have counts for
9 papers · 1 filter
Generating Visually Realistic Adversarial Patch
Xiaosen Wang, Kunyu Wang
Deep neural networks (DNNs) are vulnerable to various types of adversarial examples, bringing huge threats to security-critical applications. Among these, adversarial patches have…
Rethinking Mixup for Improving the Adversarial Transferability
Xiaosen Wang, Zeyuan Yin
Mixup augmentation has been widely integrated to generate adversarial examples with superior adversarial transferability when immigrating from a surrogate model to other models. Ho…
Structure Invariant Transformation for better Adversarial Transferability
Xiaosen Wang, Zeliang Zhang, Jianping Zhang
Given the severe vulnerability of Deep Neural Networks (DNNs) against adversarial examples, there is an urgent need for an effective adversarial attack to identify the deficiencies…
Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer
Zhijin Ge, Fanhua Shang, Hongying Liu +4
Deep neural networks are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on clean inputs. Although many attack methods can achieve high suc…
Boosting Adversarial Transferability by Block Shuffle and Rotation
Kunyu Wang, Xuanran He, Wenxuan Wang +1
Adversarial examples mislead deep neural networks with imperceptible perturbations and have brought significant threats to deep learning. An important aspect is their transferabili…
Rethinking the Backward Propagation for Adversarial Transferability
Xiaosen Wang, Kangheng Tong, Kun He
Transfer-based attacks generate adversarial examples on the surrogate model, which can mislead other black-box models without access, making it promising to attack real-world appli…
Boosting Adversarial Transferability by Achieving Flat Local Maxima
Zhijin Ge, Hongying Liu, Xiaosen Wang +2
Transfer-based attack adopts the adversarial examples generated on the surrogate model to attack various models, making it applicable in the physical world and attracting increasin…
Diversifying the High-level Features for better Adversarial Transferability
Zhiyuan Wang, Zeliang Zhang, Siyuan Liang +1
Given the great threat of adversarial attacks against Deep Neural Networks (DNNs), numerous works have been proposed to boost transferability to attack real-world applications. How…
Improving the Transferability of Adversarial Samples by Path-Augmented Method
Jianping Zhang, Jen-tse Huang, Wenxuan Wang +5
Deep neural networks have achieved unprecedented success on diverse vision tasks. However, they are vulnerable to adversarial noise that is imperceptible to humans. This phenomenon…