Results 11 to 20 of about 107,198 (316)
Adversarial consistency and the uniqueness of the adversarial bayes classifier
European Journal of Applied MathematicsMinimizing an adversarial surrogate risk is a common technique for learning robust classifiers. Prior work showed that convex surrogate losses are not statistically consistent in the adversarial context – or in other words, a minimizing sequence of the ...
Natalie S. Frank
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Adversarial symmetric GANs: Bridging adversarial samples and adversarial networks [PDF]
Neural Networks, 2021 Generative adversarial networks have achieved remarkable performance on various tasks but suffer from training instability. Despite many training strategies proposed to improve training stability, this issue remains as a challenge. In this paper, we investigate the training instability from the perspective of adversarial samples and reveal that ...
Faqiang Liu +5 more
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Combating Adversaries with Anti-adversaries
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Deep neural networks are vulnerable to small input perturbations known as adversarial attacks. Inspired by the fact that these adversaries are constructed by iteratively minimizing the confidence of a network for the true class label, we propose the anti-adversary layer, aimed at countering this effect.
Motasem Alfarra +5 more
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Adversarial Attacks on Adversarial Bandits
CoRR, 2023 Accepted by ICLR ...
Yuzhe Ma, Zhijin Zhou
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Combining Adversaries with Anti-adversaries in Training
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Adversarial training is an effective learning technique to improve the robustness of deep neural networks. In this study, the influence of adversarial training on deep learning models in terms of fairness, robustness, and generalization is theoretically investigated under more general perturbation scope that different samples can have different ...
Xiaoling Zhou, Nan Yang, Ou Wu 0001
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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.
Haizhong Zheng +4 more
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Generating Adversarial Examples with Adversarial Networks [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high ...
Chaowei Xiao +5 more
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SSRN Electronic Journal, 2022 Adversarial samples have drawn a lot of attention from the Machine Learning community in the past few years. An adverse sample is an artificial data point coming from an imperceptible modification of a sample point aiming at misleading. Surprisingly, in financial research, little has been done in relation to this topic from a concrete trading point of ...
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Modeling Adversarial Noise for Adversarial Training
, 2021 Deep neural networks have been demonstrated to be vulnerable to adversarial noise, promoting the development of defense against adversarial attacks. Motivated by the fact that adversarial noise contains well-generalizing features and that the relationship between adversarial data and natural data can help infer natural data and make reliable ...
Dawei Zhou 0004 +3 more
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Adversarial classification: An adversarial risk analysis approach [PDF]
International Journal of Approximate Reasoning, 2019 Published in the International Journal for Approximate ...
Roi Naveiro +3 more
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