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Variational Adversarial Defense: A Bayes Perspective for Adversarial Training
IEEE Transactions on Pattern Analysis and Machine IntelligenceVarious methods have been proposed to defend against adversarial attacks. However, there is a lack of enough theoretical guarantee of the performance, thus leading to two problems: First, deficiency of necessary adversarial training samples might attenuate the normal gradient's back-propagation, which leads to overfitting and gradient masking ...
Chenglong Zhao +5 more
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Multi-Scale Defense of Adversarial Images
2019 IEEE International Conference on Image Processing (ICIP), 2019Deep learning has achieved great success in image classification. However, recent researches show that existing deep learning-based classifiers remain weak for recognizing adversarial images. In this paper, an effective multi-scale defense method is proposed to solve the problem.
Jiahuan Ji, Baojiang Zhong, Kai-Kuang Ma
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Adversarial Defense in Aerial Detection
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023Yuwei Chen, Shiyong Chu
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Adversarial anchor-guided feature refinement for adversarial defense
Image and Vision Computing, 2023Hakmin Lee, Yong Man Ro
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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
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Adversarial attack and defense technologies in natural language processing: A survey
Neurocomputing, 2022Shijie Zhou, Wen Huang, Shilin Qiu
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Learning defense transformations for counterattacking adversarial examples
Neural Networks, 2023Mingkui Tan, Jiezhang Cao
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Attack-less adversarial training for a robust adversarial defense
Applied Intelligence, 2021Jiacang Ho, Byung-Gook Lee, Dae-Ki Kang
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Adversarial Sample Attack and Defense Method for Encrypted Traffic Data
IEEE Transactions on Intelligent Transportation Systems, 2022Zhiguang Qin +2 more
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