Adversarial training - Open Access .click

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Dimensionally constrained adversarial attack and defense in wind power forecasting. [PDF]

PLoS One
Min Y +5 more
europepmc +1 more source

Localized Query Attack Toward Transformer-Based Visible Object Detectors. [PDF]

Sensors (Basel)
Wang Y, Li A, Yang Z, Liu X.
europepmc +1 more source

Threats and vulnerabilities in artificial intelligence and agentic AI models. [PDF]

Front Artif Intell
Radanliev P, Santos O, Maple C.
europepmc +1 more source

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adversarial attacks
deep learning
robustness

adversarial robustness
adversarial attack
adversarial examples

data augmentation
deep neural network
adversarial machine learning

Boosting adversarial robustness via self-paced adversarial training

Neural Networks, 2023
Adversarial training is considered one of the most effective methods to improve the adversarial robustness of deep neural networks. Despite the success, it still suffers from unsatisfactory performance and overfitting. Considering the intrinsic mechanism of adversarial training, recent studies adopt the idea of curriculum learning to alleviate ...
Lirong He +5 more
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Adversarial Training With Anti-Adversaries

IEEE Transactions on Pattern Analysis and Machine Intelligence
Adversarial training is effective in improving the robustness of deep neural networks. However, existing studies still exhibit significant drawbacks in terms of the robustness, generalization, and fairness of models. In this study, we validate the importance of different perturbation directions (i.e., adversarial and anti-adversarial) and bounds from ...
Xiaoling Zhou, Ou Wu, Nan Yang
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Adversarial training with Lookahead

2022
Deep Learning wird in immer mehr sicherheitsrelevanten Bereichen wie zum Beispiel für autonomes Fahren oder in der automatischen Gesichtserkennung erfolgreich eingesetzt. Daher rückt der Sicherheitsaspekt von Deep Learning Algorithmen zunehmend in den Fokus der Forschung.
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Adversarial training

2023
Pin-Yu Chen, Cho-Jui Hsieh
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Variational Adversarial Defense: A Bayes Perspective for Adversarial Training

IEEE Transactions on Pattern Analysis and Machine Intelligence
Various 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 attacks
deep learning
robustness

adversarial robustness
adversarial attack
adversarial examples

data augmentation
deep neural network
adversarial machine learning