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Sinkhorn Adversarial Attack and Defense
IEEE Transactions on Image Processing, 2022Adversarial attacks have been extensively investigated in the recent past. Quite interestingly, a majority of these attacks primarily work in the lp space. In this work, we propose a novel approach for generating adversarial samples using Wasserstein distance.
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Robust Defense Against Adversarial Attacks with Defensive Preprocessing and Adversarial Training
2025 IEEE International Conference on Consumer Electronics (ICCE)Deep learning technologies have rapidly advanced, but concerns about their security and vulnerability to threats have emerged. Adversarial attacks, using carefully crafted perturbations, exploit these weaknesses, posing serious risks. This study introduces an integrated defensive preprocessing and adversarial training pipeline as a robust defense ...
Chih-Yang Lin +4 more
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Adversarial Attacks and Defenses
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples leaves us a big hesitation when applying DNN models on safety-critical tasks such as autonomous vehicles and malware detection.
Han Xu 0002 +3 more
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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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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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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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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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