Results 31 to 40 of about 219,753 (266)
Phase-shifted Adversarial Training
CoRR, 2023 Adversarial training has been considered an imperative component for safely deploying neural network-based applications to the real world. To achieve stronger robustness, existing methods primarily focus on how to generate strong attacks by increasing the number of update steps, regularizing the models with the smoothed loss function, and injecting the
Yeachan Kim +3 more
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A3T: Adversarially Augmented Adversarial Training
CoRR, 2018 accepted for an oral presentation in Machine Deception Workshop, NIPS ...
Akram Erraqabi +3 more
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A Survey on Efficient Methods for Adversarial Robustness
IEEE Access, 2022 Deep learning has revolutionized computer vision with phenomenal success and widespread applications. Despite impressive results in complex problems, neural networks are susceptible to adversarial attacks: small and imperceptible changes in input space ...
Awais Muhammad, Sung-Ho Bae
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Gray-Box Adversarial Training [PDF]
, 2018 Adversarial samples are perturbed inputs crafted to mislead the machine learning systems. A training mechanism, called adversarial training, which presents adversarial samples along with clean samples has been introduced to learn robust models. In order to scale adversarial training for large datasets, these perturbations can only be crafted using fast
Vivek B. S. +2 more
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Adversarial Training for Commonsense Inference [PDF]
Proceedings of the 5th Workshop on Representation Learning for NLP, 2020 6 pages, Accepted to ACL2020 RepL4NLP ...
Lis Pereira +4 more
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Deep Learning Based Robust Text Classification Method via Virtual Adversarial Training
IEEE Access, 2020 The existing methods of generating adversarial texts usually change the original meanings of texts significantly and even generate the unreadable texts.
Wei Zhang, Qian Chen, Yunfang Chen
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Target Training Does Adversarial Training Without Adversarial Samples
CoRR, 2021 arXiv admin note: text overlap with arXiv:2006 ...
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EIFDAA: Evaluation of an IDS with function-discarding adversarial attacks in the IIoT
Heliyon, 2023 The complexity of the Industrial Internet of Things (IIoT) presents higher requirements for intrusion detection systems (IDSs). An adversarial attack is a threat to the security of machine learning-based IDSs.
Shiming Li +4 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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Fast-M Adversarial Training Algorithm for Deep Neural Networks
Applied SciencesAlthough deep neural networks have been successfully applied in many fields, research studies show that neural network models are easily disrupted by small malicious inputs, greatly reducing their performance.
Yu Ma +4 more
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