Results 31 to 40 of about 34,476 (259)
Probabilistic Categorical Adversarial Attack & Adversarial Training
CoRR, 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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Universal Adversarial Training
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Standard adversarial attacks change the predicted class label of a selected image by adding specially tailored small perturbations to its pixels. In contrast, a universal perturbation is an update that can be added to any image in a broad class of images, while still changing the predicted class label.
Ali Shafahi +5 more
openaire +3 more sources
Pre-Trained Adversarial Perturbations
Advances in Neural Information Processing Systems 35, 2022 Self-supervised pre-training has drawn increasing attention in recent years due to its superior performance on numerous downstream tasks after fine-tuning. However, it is well-known that deep learning models lack the robustness to adversarial examples, which can also invoke security issues to pre-trained models, despite being less explored.
Yuanhao Ban, Yinpeng Dong
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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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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
doaj +1 more source
CAT:Collaborative Adversarial Training
CoRR, 2023 Tech ...
Xingbin Liu +4 more
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Exploring generative adversarial networks and adversarial training
International Journal of Cognitive Computing in Engineering, 2022 Recognized as a realistic image generator, Generative Adversarial Network (GAN) occupies a progressive section in deep learning. Using generative modeling, the underlying generator model learns the real target distribution and outputs fake samples from ...
Afia Sajeeda, B M Mainul Hossain, Ph.D
doaj +1 more source
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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Adversarial Training for Free!
CoRR, 2019 ISBN:978-1-7138-0793 ...
Ali Shafahi +8 more
openaire +3 more sources