  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.
