Results 21 to 30 of about 34,476 (259)
Calibrated Adversarial Training
CoRR, 2021 ACML 2021 accepted,24 ...
Tianjin Huang +3 more
openaire +4 more sources
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
openaire +2 more sources
Applied Sciences, 2023 In this paper, we propose an advanced method for adversarial training that focuses on leveraging the underlying structure of adversarial perturbation distributions. Unlike conventional adversarial training techniques that consider adversarial examples in
Bader Rasheed +2 more
doaj +1 more source
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
openaire +2 more sources
Recent Advances in Adversarial Training for Adversarial Robustness [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Adversarial training is one of the most effective approaches for deep learning models to defend against adversarial examples. Unlike other defense strategies, adversarial training aims to enhance the robustness of models intrinsically. During the past few years, adversarial training has been studied and discussed from various aspects, which deserves ...
Tao Bai +4 more
openaire +2 more sources
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 CVPR2022
Tao Li 0054 +4 more
openaire +2 more sources
Self-Supervised Adversarial Training [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Recent work has demonstrated that neural networks are vulnerable to adversarial examples. To escape from the predicament, many works try to harden the model in various ways, in which adversarial training is an effective way which learns robust feature representation so as to resist adversarial attacks.
Kejiang Chen +8 more
openaire +2 more sources
IEEE Access, 2022 Adversarial training is exploited to develop a robust Deep Neural Network (DNN) model against the malicious altered data. These attacks may have catastrophic effects on DNN models but are indistinguishable for a human being.
Farzad Nikfam +3 more
doaj +1 more source
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
openaire +3 more sources
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
doaj +1 more source