Adversarial training in logit space against tiny perturbations-Reference-Cited by-同舟云学术

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Adversarial training in logit space against tiny perturbations

Published:2023-10-17 Issue:6 Volume:29 Page:3277-3290
ISSN:0942-4962
Container-title:Multimedia Systems
language:en
Short-container-title:Multimedia Systems

Author:

Guan Xiaohui,Shao Qiqi,Qian Yaguan,Yao Tengteng,Wang Bin

Funder

Zhejiang Provincial Natural Science Foundation of China

National Key R &D Program of China

Natural Science Foundation of China

Major Research Plan of the National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s00530-023-01193-9.pdf

Reference49 articles.

1. Wu, M., Chen, L.: Image recognition based on deep learning. In: 2015 Chinese Automation Congress, pp. 542–546 (2015)

2. Noda, K., Yamaguchi, Y., Nakadai, K., Okuno, H.G., Ogata, T.: Audio-visual speech recognition using deep learning. Appl. Intell. (2015)

3. Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167 (2008)

4. Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., Song, D.: Robust physical-world attacks on deep learning visual classification. In: The IEEE Conference on Computer Vision and Pattern Recognition (2018)

5. Boloor, A., He, X., Gill, C., Yevgeniy, V., Xuan, Z.: Simple physical adversarial examples against end-to-end autonomous driving models. In: The IEEE International Conference on Embedded Software and Systems (ICESS) (2019)

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