Results 11 to 20 of about 217,331 (267)
Adversarial Training for Free!
, 2019 Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks.
Davis, Larry S. +8 more
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Neural Networks, 2023 Adversarial robustness is considered as a required property of deep neural networks. In this study, we discover that adversarially trained models might have significantly different characteristics in terms of margin and smoothness, even they show similar robustness.
Hoki Kim +3 more
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Self-Supervised Adversarial Training [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Recent work has demonstrated that neural networks are vulnerable to adversarial examples. To escape from the predicament, many works try to harden the model in various ways, in which adversarial training is an effective way which learns robust feature representation so as to resist adversarial attacks.
Chen, Kejiang +8 more
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Efficient Adversarial Training With Transferable Adversarial Examples [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require orders of magnitude additional training time due to high cost of generating strong adversarial examples during training.
Zheng, Haizhong +4 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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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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2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 CVPR2022
Li, Tao +4 more
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