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2019

Security of Deep Learning Methodologies: Challenges and Opportunities

Rezaei, Shahbaz, Liu, Xin

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Despite the plethora of studies about security vulnerabilities and defenses of deep learning models, security aspects of deep learning methodologies, such as transfer learning, have been rarely studied.

In this article, we highlight the security challenges and research opportunities of these methodologies, focusing on vulnerabilities and attacks unique to them.

Security of Deep Learning Methodologies: Challenges and Opportunities · Around

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B. Wang, Y. Yao, B. Viswanath, H. Zheng, and B. Y. Zhao, “With great training comes great vulnerability: practical attacks against transfer learning,” in 27th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 18) , 2018, pp. 1281–1297
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S. Rezaei and X. Liu, “A target-agnostic attack on deep models: Exploiting security vulnerabilities of transfer learning,” arXiv preprint arXiv:1904.04334 , 2019
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Y. Ji, X. Zhang, S. Ji, X. Luo, and T. Wang, “Model-reuse attacks on deep learning systems,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2018, pp. 349–363

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