Results 11 to 20 of about 17,780 (281)
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 Attack with Raindrops
CoRR, 2023 10 pages, 7 figures, This manuscript was submitted to CVPR ...
Jiyuan Liu 0005 +4 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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Remote Sensing, 2023 Aerial Image Semantic segmentation based on convolution neural networks (CNNs) has made significant process in recent years. Nevertheless, their vulnerability to adversarial example attacks could not be neglected.
Zhen Wang +3 more
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Adversarial attacks and defenses in deep learning
网络与信息安全学报, 2020 The adversarial example is a modified image that is added imperceptible perturbations, which can make deep neural networks decide wrongly. The adversarial examples seriously threaten the availability of the system and bring great security risks to the ...
LIU Ximeng +2 more
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CoRR, 2022 ICML Workshop 2022 on Adversarial Machine Learning ...
Soichiro Kumano +2 more
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TextFirewall: Omni-Defending Against Adversarial Texts in Sentiment Classification
IEEE Access, 2021 Sentiment classification has been broadly applied in real life, such as product recommendation and opinion-oriented analysis. Unfortunately, the widely employed sentiment classification systems based on deep neural networks (DNNs) are susceptible to ...
Wenqi Wang +3 more
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CoRR, 2022 Recent advances in machine learning show that neural models are vulnerable to minimally perturbed inputs, or adversarial examples. Adversarial algorithms are optimization problems that minimize the accuracy of ML models by perturbing inputs, often using a model's loss function to craft such perturbations.
Thomas Cilloni +2 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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