Adversarial Examples in Constrained Domains

Ryan Sheatsley
Ryan Sheatsley
Michael Weisman
Michael Weisman
Gunjan Verma
Gunjan Verma

2020.

Cited by: 0|Bibtex|Views22
Other Links: arxiv.org|academic.microsoft.com

Abstract:

Machine learning algorithms have been shown to be vulnerable to adversarial manipulation through systematic modification of inputs (e.g., adversarial examples) in domains such as image recognition. Under the default threat model, the adversary exploits the unconstrained nature of images; each feature (pixel) is fully under control of th...More

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