  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.
