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Multiuser Adversarial Attack on Deep Learning for OFDM Detection [PDF]

IEEE Wireless Communications Letters, 2022
Adversarial attack has been widely used to degrade the performance of deep learning (DL), especially in the field of communications. In this letter, we evaluate different white-box and black-box adversarial attack algorithms for a DL-based multiuser ...
Mingqian Liu, Youjie Ye
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Distilling Knowledge in Adversarial Attack

2020 7th International Conference on Dependable Systems and Their Applications (DSA), 2020
Neural networks show great vulnerability under the threat of adversarial examples. By adding small perturbation to a clean image, neural networks with high classification accuracy can be completely fooled. Transferability which allows adversarial examples to transfer to networks of unknown structures, makes adversarial examples even more harmful.
Zeqian Dong, Long Tang, Cong Tian 0001
openaire   +2 more sources

Adversarial Attacks and Defenses on Graphs

ACM SIGKDD Explorations Newsletter, 2021
Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the input, called adversarial attacks.
Wei Jin 0009, Yaxin Li 0001, Han Xu 0002, Yiqi Wang 0001, Shuiwang Ji, Charu Aggarwal 0001, Jiliang Tang   +6 more
openaire   +1 more source

Adversarial Attack on Video Retrieval

2020 The 4th International Conference on Video and Image Processing, 2020
Recently adversarial examples have been reported to reveal the fragility of deep learning models. However, most adversarial attacks focus on classification task and less attention has been paid to retrieval task. In this paper, we are the first to investigate adversarial examples on the video retrieval system in both non-targeted and targeted attack ...
Ying Zou 0008, Chenglong Zhao, Bingbing Ni   +2 more
openaire   +1 more source

Generative Transferable Adversarial Attack

Proceedings of the 3rd International Conference on Video and Image Processing, 2019
Despite their superior performance in computer vision tasks, deep neural networks are found to be vulnerable to adversarial examples, slightly perturbed examples that can mislead trained models. Moreover, adversarial examples are often transferable, i.e., adversaries crafted for one model can attack another model.
Yifeng Li, Ya Zhang 0002, Rui Zhang, Yanfeng Wang 0001   +3 more
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Componentwise Adversarial Attacks

2023
Lucas Beerens, Desmond J. Higham
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A Survey of Adversarial Attack and Defense Methods for Malware Classification in Cyber Security

IEEE Communications Surveys and Tutorials, 2023
Quan Yu
exaly  

Adversarial Attack and Defense: A Survey

Electronics (Switzerland), 2022
Erlu He, Yangyang Zhao
exaly  

Adversarial Attack Type I: Cheat Classifiers by Significant Changes

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Chengjin Sun, Mingjian Chen, Xiaolin Huang   +2 more
exaly