30 citations · 100 across the 18 of their papers we have counts for
18 papers
Devling into Adversarial Transferability on Image Classification: Review, Benchmark, and Evaluation
Xiaosen Wang, Zhijin Ge, Bohan Liu +5
Adversarial transferability refers to the capacity of adversarial examples generated on the surrogate model to deceive alternate, unexposed victim models. This property eliminates…
ViT-EnsembleAttack: Augmenting Ensemble Models for Stronger Adversarial Transferability in Vision Transformers
Hanwen Cao, Haobo Lu, Xiaosen Wang +1
Ensemble-based attacks have been proven to be effective in enhancing adversarial transferability by aggregating the outputs of models with various architectures. However, existing…
Disrupting Semantic and Abstract Features for Better Adversarial Transferability
Yuyang Luo, Xiaosen Wang, Zhijin Ge +1
Adversarial examples pose significant threats to deep neural networks (DNNs), and their property of transferability in the black-box setting has led to the emergence of transfer-ba…
Boosting the Local Invariance for Better Adversarial Transferability
Bohan Liu, Xiaosen Wang
Transfer-based attacks pose a significant threat to real-world applications by directly targeting victim models with adversarial examples generated on surrogate models. While numer…
Bag of Tricks to Boost Adversarial Transferability
Zeliang Zhang, Wei Yao, Xiaosen Wang
Deep neural networks are widely known to be vulnerable to adversarial examples. However, vanilla adversarial examples generated under the white-box setting often exhibit low transf…
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…
Robust Textual Embedding against Word-level Adversarial Attacks
Yichen Yang, Xiaosen Wang, Kun He
We attribute the vulnerability of natural language processing models to the fact that similar inputs are converted to dissimilar representations in the embedding space, leading to…
TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack
Zhen Yu, Xiaosen Wang, Wanxiang Che +1
Existing textual adversarial attacks usually utilize the gradient or prediction confidence to generate adversarial examples, making it hard to be deployed in real-world application…
Detecting Textual Adversarial Examples through Randomized Substitution and Vote
Xiaosen Wang, Yifeng Xiong, Kun He
A line of work has shown that natural text processing models are vulnerable to adversarial examples. Correspondingly, various defense methods are proposed to mitigate the threat of…
AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples
Xiaosen Wang, Kun He, Chuanbiao Song +2
Despite the rapid development of adversarial machine learning, most adversarial attack and defense researches mainly focus on the perturbation-based adversarial examples, which is…