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Variational Adversarial Defense: A Bayes Perspective for Adversarial Training

IEEE Transactions on Pattern Analysis and Machine Intelligence
Various 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
openaire   +2 more sources

Multi-Scale Defense of Adversarial Images

2019 IEEE International Conference on Image Processing (ICIP), 2019
Deep 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
openaire   +1 more source

Adversarial Defense in Aerial Detection

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023
Yuwei Chen, Shiyong Chu
openaire   +1 more source

Adversarial anchor-guided feature refinement for adversarial defense

Image and Vision Computing, 2023
Hakmin Lee, Yong Man Ro
openaire   +1 more source

Causal Disentanglement for Adversarial Defense

2023
Ji-Young Park   +3 more
openaire   +1 more source

Adversarial attack and defense technologies in natural language processing: A survey

Neurocomputing, 2022
Shijie Zhou, Wen Huang, Shilin Qiu
exaly  

Attack-less adversarial training for a robust adversarial defense

Applied Intelligence, 2021
Jiacang Ho, Byung-Gook Lee, Dae-Ki Kang
openaire   +1 more source

Adversarial Sample Attack and Defense Method for Encrypted Traffic Data

IEEE Transactions on Intelligent Transportation Systems, 2022
Zhiguang Qin   +2 more
exaly  

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