  Deep neural networks (DNNs) have achieved tremendous success in various
applications including video action recognition, yet remain vulnerable to
backdoor attacks (Trojans). The backdoor-compromised model will mis-classify to
the target class chosen by the attacker when a test instance (from a non-target
class) is embedded with a specific trigger, while maintaining high accuracy on
attack-free instances. Although there are extensive studies on backdoor attacks
against image data, the susceptibility of video-based systems under backdoor
attacks remains largely unexplored. Current studies are direct extensions of
approaches proposed for image data, e.g., the triggers are independently
embedded within the frames, which tend to be detectable by existing defenses.
In this paper, we introduce a simple yet effective backdoor attack against
video data. Our proposed attack, adding perturbations in a transformed domain,
plants an imperceptible, temporally distributed trigger across the video
frames, and is shown to be resilient to existing defensive strategies. The
effectiveness of the proposed attack is demonstrated by extensive experiments
with various well-known models on two video recognition benchmarks, UCF101 and
HMDB51, and a sign language recognition benchmark, Greek Sign Language (GSL)
dataset. We delve into the impact of several influential factors on our
proposed attack and identify an intriguing effect termed "collateral damage"
through extensive studies.
