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发挥着重要作用,能够在满足所有底层带宽限制的同时有效传输视觉数据。基于深度学习的视频压缩方法正在迅速取代传统算法,并在边缘设备上提供最先进的结果。然而,最近开发的对抗性攻击证明了数字上制作的扰动可以破坏视频压缩的速率-失真关系。在这项工作中,我们提出了一种实际可行的LED攻击,以针对视频压缩框架。我们称此物理现实攻击为NetFlick,可以通过注入闪烁的时间扰动来破坏连续帧之间的时空相关性。此外,我们提出了通用扰动,可以降低对流入视频的性能,而无需事先了解内容。实验结果表明,NetFlick可以成功地在数字和物理环境中破坏视频压缩框架的性能,并且可以进一步扩展以攻击下游视频分类网络。