  Online video platforms receive hundreds of hours of uploads every minute,
making manual content moderation impossible. Unfortunately, the most vulnerable
consumers of malicious video content are children from ages 1-5 whose attention
is easily captured by bursts of color and sound. Scammers attempting to
monetize their content may craft malicious children's videos that are
superficially similar to educational videos, but include scary and disgusting
characters, violent motions, loud music, and disturbing noises. Prominent video
hosting platforms like YouTube have taken measures to mitigate malicious
content on their platform, but these videos often go undetected by current
content moderation tools that are focused on removing pornographic or
copyrighted content. This paper introduces our toolkit Malicious or Benign for
promoting research on automated content moderation of children's videos. We
present 1) a customizable annotation tool for videos, 2) a new dataset with
difficult to detect test cases of malicious content and 3) a benchmark suite of
state-of-the-art video classification models.
