  In recent years, video analysis tools for automatically extracting meaningful
information from videos are widely studied and deployed. Because most of them
use deep neural networks which are computationally expensive, feeding only a
subset of video frames into such algorithms is desired. Sampling the frames
with fixed rate is always attractive for its simplicity, representativeness,
and interpretability. For example, a popular cloud video API generated video
and shot labels by processing only the first frame of every second in a video.
However, one can easily attack such strategies by placing chosen frames at the
sampled locations. In this paper, we present an elegant solution to this
sampling problem that is provably robust against adversarial attacks and
introduces bounded irregularities as well.
