Results 21 to 30 of about 505,564 (200)
On the Reversibility of Adversarial Attacks
2021 IEEE International Conference on Image Processing (ICIP), 2021 Adversarial attacks modify images with perturbations that change the prediction of classifiers. These modified images, known as adversarial examples, expose the vulnerabilities of deep neural network classifiers. In this paper, we investigate the predictability of the mapping between the classes predicted for original images and for their corresponding
Chau Yi Li +4 more
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Adversarial Attacks on Adversarial Bandits
CoRR, 2023 Accepted by ICLR ...
Yuzhe Ma, Zhijin Zhou
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A Survey on Universal Adversarial Attack [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 The intriguing phenomenon of adversarial examples has attracted significant attention in machine learning and what might be more surprising to the community is the existence of universal adversarial perturbations (UAPs), i.e. a single perturbation to fool the target DNN for most images.
Chaoning Zhang +5 more
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Attacking Adversarial Attacks as A Defense
CoRR, 2021 It is well known that adversarial attacks can fool deep neural networks with imperceptible perturbations. Although adversarial training significantly improves model robustness, failure cases of defense still broadly exist. In this work, we find that the adversarial attacks can also be vulnerable to small perturbations.
Boxi Wu +8 more
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Adversarial Robustness of Deep Reinforcement Learning Based Dynamic Recommender Systems
Frontiers in Big Data, 2022 Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including interactive recommendation systems.
Siyu Wang +5 more
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Stochastic sparse adversarial attacks [PDF]
2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), 2021 This paper introduces stochastic sparse adversarial attacks (SSAA), standing as simple, fast and purely noise-based targeted and untargeted attacks of neural network classifiers (NNC). SSAA offer new examples of sparse (or $L_0$) attacks for which only few methods have been proposed previously.
Hajri, Hatem +4 more
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Adversarial Attack with Raindrops
CoRR, 2023 10 pages, 7 figures, This manuscript was submitted to CVPR ...
Jiyuan Liu 0005 +4 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness. In practice, attack algorithms are artificially selected and tuned by human experts to break a ML system. However, manual selection of attackers tends to be sub-optimal, leading to a mistakenly assessment of model ...
Xiaofeng Mao +5 more
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A Multimodal Adversarial Attack Framework Based on Local and Random Search Algorithms
International Journal of Computational Intelligence Systems, 2021 Although many problems in computer vision and natural language processing have made breakthrough progress with neural networks, adversarial attack is a serious potential problem in many neural network- based applications.
Zibo Yi, Jie Yu, Yusong Tan, Qingbo Wu
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Object Detection Adversarial Attack for Infrared Imagery in Remote Sensing [PDF]
Hangkong bingqi, 2022 Aiming at the problems of poor effect of existing adversarial attack for object detection algorithms on small-scale target attack, a large number of meaningless disturbances in adversarial samples and low disturbance genera-tion efficiency, taking ...
Qi Jiahao, Zhang Yu, Wan Pengcheng, Li Yuanzhe, Liu Xingyue, Yao Aihuan, Zhong Ping
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