Heuristic Black-box Adversarial Attacks on Video Recognition Models

Understand

We study the problem of attacking video recognition models in the black-box setting, where the model information is unknown and the adversary can only make queries to detect the predicted top-1 class and its probability.

• Compared with the black-box attack on images, attacking videos is more challenging as the computation cost for searching the adversarial perturbations on a video is much higher due to its high dimensionality.
• To overcome this challenge, we propose a heuristic black-box attack model that generates adversarial perturbations only on the selected frames and regions.
• More specifically, a heuristic-based algorithm is proposed to measure the importance of each frame in the video towards generating the adversarial examples.