Results 21 to 30 of about 219,753 (266)
Detecting High-Resolution Adversarial Images with Few-Shot Deep Learning
Remote Sensing, 2023 Deep learning models have enabled significant performance improvements to remote sensing image processing. Usually, a large number of training samples is required for detection models.
Junjie Zhao +4 more
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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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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
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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
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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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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
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CAT:Collaborative Adversarial Training
CoRR, 2023 Tech ...
Xingbin Liu +4 more
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Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives [PDF]
, 2018 Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains.
Cummins, Nicholas +3 more
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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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