Scaling up the Randomized Gradient-Free Adversarial Attack Reveals Overestimation of Robustness Using Established Attacks-Reference-Cited by-同舟云学术

Scaling up the Randomized Gradient-Free Adversarial Attack Reveals Overestimation of Robustness Using Established Attacks

Published:2019-10-03 Issue:4 Volume:128 Page:1028-1046
ISSN:0920-5691
Container-title:International Journal of Computer Vision
language:en
Short-container-title:Int J Comput Vis

Author:

Croce Francesco,Rauber Jonas,Hein Matthias

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s11263-019-01213-0.pdf

Reference38 articles.

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4. Brendel, W., Rauber, J., & Bethge, M. (2018). Decision-based adversarial attacks: Reliable attacks against black-box machine learning models. In ICLR.

5. Carlini, N., & Wagner, D. (2017a). Adversarial examples are not easily detected: Bypassing ten detection methods. In ACM workshop on artificial intelligence and security.

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