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Evaluating Transfer-based Targeted Adversarial Perturbations against Real-World Computer Vision Systems based on Human Judgments. (arXiv:2206.01467v1 [cs.CV])
June 6, 2022, 1:20 a.m. | Zhengyu Zhao, Nga Dang, Martha Larson
cs.CR updates on arXiv.org arxiv.org
Computer vision systems are remarkably vulnerable to adversarial
perturbations. Transfer-based adversarial images are generated on one (source)
system and used to attack another (target) system. In this paper, we take the
first step to investigate transfer-based targeted adversarial images in a
realistic scenario where the target system is trained on some private data with
its inventory of semantic labels not publicly available. Our main contributions
include an extensive human-judgment-based evaluation of attack success on the
Google Cloud Vision API and …
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