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DeepRobust: a Platform for Adversarial Attacks and Defenses
Proceedings of the AAAI Conference on Artificial Intelligence, 2021DeepRobust is a PyTorch platform for generating adversarial examples and building robust machine learning models for different data domains. Users can easily evaluate the attack performance against different defense methods with DeepRobust and get performance analyzing visualization.
Yaxin Li 0001 +3 more
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Adversarial Example Defense Based on the Supervision
2021 International Joint Conference on Neural Networks (IJCNN), 2021In recent years, deep learning has developed rapidly and has shown great performance on many challenging machine learning tasks, such as image classification, natural language processing, and speech recognition. However, researchers have recently discovered that deep learning models have security risks and are easily affected by adversarial examples ...
Ziyu Yao 0003, Jiaquan Gao
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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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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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MTD-AD: Moving Target Defense as Adversarial Defense
IEEE Transactions on Dependable and Secure ComputingNetwork Intrusion Detection Systems (NIDSes) are increasingly incorporating Machine Learning (ML) and Deep Learning (DL) algorithms for detecting network intrusions. However, ML/DL algorithms are susceptible to adversarial examples, which can lead to the misclassification of input data.
Ke He +2 more
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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, 2023Senming Yan, Wei Wang, Limin Sun
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Adversarial attack and defense technologies in natural language processing: A survey
Neurocomputing, 2022Shilin Qiu, Qihe Liu, Shijie Zhou
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