Stealthy Adversarial Perturbations Against Real-Time Video Classification Systems

Recent research has demonstrated the brittleness of machine learning systems to adversarial perturbations. However, the studies have been mostly limited to perturbations on images and more generally, classification tasks that do not deal with real-time stream inputs. In this paper we ask ”Are adversarial perturbations that cause misclassification in real-time video classification systems possible, and if so what properties must they satisfy?” Real-time video classification systems find application in surveillance applications, smart vehicles, and smart elderly care and thus, misclassification could be particularly harmful (e.g., a mishap at an elderly care facility may be missed). Video classification systems take video clips as inputs and these clip boundaries are not deterministic. We show that perturbations that do not take “the indeterminism in the clip boundaries input to the video classifier” into account, do not achieve high attack success rates. We propose novel approaches for generating 3D adversarial perturbations (perturbation clips) that exploit recent advances in generative models to not only overcome this key challenge but also provide stealth. In particular, our most potent 3D adversarial perturbations cause targeted activities in video streams to be misclassified with rates over 80%. At the same time, they also ensure that the perturbations leave other (untargeted) activities largely unaffected making them extremely stealthy. Finally, we also derive a single-frame (2D) perturbation that can be applied to every frame in a video stream, and which in many cases, achieves extremely high misclassification rates.