Inter-frame Accelerate Attack against Video Interpolation Models
Junpei Liao Zhikai Chen Liang Yi Wenyuan Yang Baoyuan Wu Xiaochun Cao
Deep learning based video frame interpolation (VIF) method, aiming to
synthesis the intermediate frames to enhance video quality, have been highly
developed in the past few years. This paper investigates the adversarial
robustness of VIF models. We apply adversarial attacks to VIF models and find
that the VIF models are very vulnerable to adversarial examples. To improve
attack efficiency, we suggest to make full use of the property of video frame
interpolation task. The intuition is that the gap between adjacent frames would
be small, leading to the corresponding adversarial perturbations being similar
as well. Then we propose a novel attack method named Inter-frame Accelerate
Attack (IAA) that initializes the perturbation as the perturbation for the
previous adjacent frame and reduces the number of attack iterations. It is
shown that our method can improve attack efficiency greatly while achieving
comparable attack performance with traditional methods. Besides, we also extend
our method to video recognition models which are higher level vision tasks and
achieves great attack efficiency.