Video compression plays a significant role in IoT devices for the efficient transport of visual data while satisfying all underlying bandwidth constraints. Deep learning-based video compression methods are rapidly replacing traditional algorithms and providing state-of-the-art results on edge devices. However, recently developed adversarial attacks demonstrate that digitally crafted perturbations can break the Rate-Distortion relationship of video compression. In this work, we present a real-world LED attack to target video compression frameworks. Our physically realizable attack, dubbed NetFlick, can degrade the spatio-temporal correlation between successive frames by injecting flickering temporal perturbations. In addition, we propose universal perturbations that can downgrade performance of incoming video without prior knowledge of the contents. Experimental results demonstrate that NetFlick can successfully deteriorate the performance of video compression frameworks in both digital- and physical-settings and can be further extended to attack downstream video classification networks.

翻译：视频压缩在物联网设备中扮演着重要角色，它能在满足所有底层带宽约束的同时高效传输视觉数据。基于深度学习的视频压缩方法正在迅速取代传统算法，并在边缘设备上实现最先进的结果。然而，近期开发的对抗性攻击表明，数字精心设计的扰动能够破坏视频压缩的率失真关系。本研究提出了一种面向真实世界的LED攻击方法，专门针对视频压缩框架。我们提出的物理可实现攻击（称为NetFlick）通过注入闪烁时间扰动，可破坏连续帧之间的时空相关性。此外，我们提出了一种通用扰动，无需预先了解内容就能降低输入视频的性能。实验结果表明，NetFlick在数字环境和物理环境中均能成功削弱视频压缩框架的性能，并可进一步扩展用于攻击下游视频分类网络。