Results 11 to 20 of about 215,342 (268)
Applied Sciences, 2023 In this paper, we propose an advanced method for adversarial training that focuses on leveraging the underlying structure of adversarial perturbation distributions. Unlike conventional adversarial training techniques that consider adversarial examples in
Bader Rasheed +2 more
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IEEE Access, 2022 Adversarial training is exploited to develop a robust Deep Neural Network (DNN) model against the malicious altered data. These attacks may have catastrophic effects on DNN models but are indistinguishable for a human being.
Farzad Nikfam +3 more
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Boosting Fast Adversarial Training With Learnable Adversarial Initialization
IEEE Transactions on Image Processing, 2022 Accepted by ...
Xiaojun Jia +4 more
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Adversarially-Trained Nonnegative Matrix Factorization [PDF]
IEEE Signal Processing Letters, 2021 We consider an adversarially-trained version of the nonnegative matrix factorization, a popular latent dimensionality reduction technique. In our formulation, an attacker adds an arbitrary matrix of bounded norm to the given data matrix. We design efficient algorithms inspired by adversarial training to optimize for dictionary and coefficient matrices ...
Cai, Ting +2 more
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Detecting High-Resolution Adversarial Images with Few-Shot Deep Learning
Remote Sensing, 2023 Deep learning models have enabled significant performance improvements to remote sensing image processing. Usually, a large number of training samples is required for detection models.
Junjie Zhao +4 more
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Curriculum Adversarial Training [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Recently, deep learning has been applied to many security-sensitive applications, such as facial authentication. The existence of adversarial examples hinders such applications. The state-of-the-art result on defense shows that adversarial training can be applied to train a robust model on MNIST against adversarial examples; but it fails to achieve a ...
Qi-Zhi Cai, Chang Liu, Dawn Song
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Probabilistic Categorical Adversarial Attack & Adversarial Training
, 2022 The existence of adversarial examples brings huge concern for people to apply Deep Neural Networks (DNNs) in safety-critical tasks. However, how to generate adversarial examples with categorical data is an important problem but lack of extensive exploration.
Xu, Han +6 more
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Adversarial Training Against Location-Optimized Adversarial Patches [PDF]
, 2020 20 pages, 6 tables, 4 figures, 2 algorithms, European Conference on Computer Vision Workshops ...
Sukrut Rao, David Stutz, Bernt Schiele
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Towards Adversarial Robustness for Multi-Mode Data through Metric Learning
Sensors, 2023 Adversarial attacks have become one of the most serious security issues in widely used deep neural networks. Even though real-world datasets usually have large intra-variations or multiple modes, most adversarial defense methods, such as adversarial ...
Sarwar Khan +3 more
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2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 CVPR2022
Li, Tao +4 more
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