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.

研究通过对视频帧差值 (VIF) 模型的对抗性攻击，提出了使用视频帧差值的性质对攻击进行改进的方法，并提出了一种名为 “帧间加速攻击”(IAA) 的新型攻击方法，该方法可以显著提高攻击效率。同时，还将这种方法扩展到更高级别的视频识别模型，并取得了很好的攻击效果。