Evaluating Transfer-based Targeted Adversarial Perturbations against Real-World Computer Vision Systems based on Human Judgments
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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 additional analysis of the different behaviors of Google Cloud Vision in face of original images vs. adversarial images. Resources are publicly available at <https://github.com/ZhengyuZhao/Targeted-Tansfer/blob/main/google_results.zip>.
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