Efficient Sparse Attacks on Videos using Reinforcement Learning
Huanqian Yan, Xingxing Wei · 2021
More and more deep neural network models have been deployed in real-time video systems. However, it is proved that deep models are susceptible to the crafted adversarial examples. The adversarial examples are imperceptible and can make the normal deep models misclassify them. Although there exist a few works aiming at the adversarial examples of video recognition in the black-box attack mode, most of them need large perturbations or hundreds of thousands of queries. There are still lack of effective adversarial methods to produce adversarial videos with small perturbations and limited query numbers at the same time.