Advances in Adversarial Attacks (AA)

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Anouncements

- This repository provides references to recent advances in adversarial attacks (AA) of intelligent systems, and will be updated once every month with the hope of expediting the development of this field.

- The main body of this repository consists of 4 components: (0) tutorials and reviews; (1) advances in attacking methods section which contains references to different methods for performing adversarial attacks such as evasion attacks; (2) advances in defending methods section which contains references to different methods for securing intelligent systems such as adversarial training; (3) empirical advances such as distance from data samples to decision boundary; (4) theoretical advances such as quantifications of vulnerabilities and convergence analysis of adversarial training.

- This repository won't be possible without the efforts from many contributors who are listed in the end. If you want to contribute to this repository, you can simply put the reference information in the comment for this repository or send us an email. Please follow the following formats to help us: (1) send emails to yijirong@hotmail.com ; (2) set the email title as "Refrences_AA_Institute"; (3) set the references format as Vancouver (available in Google Scholar) with hyperlinks to the reference and its implementation (if it's available), i.e.,

Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199. 2013 Dec 21. Github

 

- If you have any constructive suggestions, please leave them as comments to this repository.

 

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Tutorials and Reviews

- Zhang XY, Liu CL, Suen CY. Towards Robust Pattern Recognition: A Review. Proceedings of the IEEE. 2020 May 28;108(6):894-922.

- Silva SH, Najafirad P. Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey. arXiv preprint arXiv:2007.00753. 2020 Jul 1.

- Chaubey A, Agrawal N, Barnwal K, Guliani KK, Mehta P. Universal Adversarial Perturbations: A Survey. arXiv preprint arXiv:2005.08087. 2020 May 16.

- Hao-Chen, Han Xu Yao Ma, Liu Debayan Deb, Hui Liu Ji-Liang Tang Anil, and K. Jain. "Adversarial attacks and defenses in images, graphs and text: A review." International Journal of Automation and Computing 17, no. 2 (2020): 151-178.

- Wiyatno RR, Xu A, Dia O, de Berker A. Adversarial Examples in Modern Machine Learning: A Review. arXiv preprint arXiv:1911.05268. 2019 Nov 13.

- Akhtar N, Mian A. Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access. 2018 Feb 19;6:14410-30.

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Advances in Attacking Methods

 

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Advances in Defending Methods

 

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Empirical Advances

 

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Theoretical Advances

 

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Contributors

This repository will be impossible without the contributions from the following:

* UserID, Affiliation, contributing since, number of reference contribution

 

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References