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Consistent Semantic Attacks on Optical Flow

2021-11-16 14:05:07
Tom Koren, Lior Talker, Michael Dinerstein, Roy J Jevnisek

Abstract

We present a novel approach for semantically targeted adversarial attacks on Optical Flow. In such attacks the goal is to corrupt the flow predictions of a specific object category or instance. Usually, an attacker seeks to hide the adversarial perturbations in the input. However, a quick scan of the output reveals the attack. In contrast, our method helps to hide the attackers intent in the output as well. We achieve this thanks to a regularization term that encourages off-target consistency. We perform extensive tests on leading optical flow models to demonstrate the benefits of our approach in both white-box and black-box settings. Also, we demonstrate the effectiveness of our attack on subsequent tasks that depend on the optical flow.

Abstract (translated)

URL

https://arxiv.org/abs/2111.08485

PDF

https://arxiv.org/pdf/2111.08485.pdf


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