  To address the increasing need for efficient and accurate content moderation,
we propose an efficient and lightweight deep classification ensemble structure.
Our approach is based on a combination of simple visual features, designed for
high-accuracy classification of violent content with low false positives. Our
ensemble architecture utilizes a set of lightweight models with narrowed-down
color features, and we apply it to both images and videos.
  We evaluated our approach using a large dataset of explosion and blast
contents and compared its performance to popular deep learning models such as
ResNet-50. Our evaluation results demonstrate significant improvements in
prediction accuracy, while benefiting from 7.64x faster inference and lower
computation cost.
  While our approach is tailored to explosion detection, it can be applied to
other similar content moderation and violence detection use cases as well.
Based on our experiments, we propose a "think small, think many" philosophy in
classification scenarios. We argue that transforming a single, large,
monolithic deep model into a verification-based step model ensemble of multiple
small, simple, and lightweight models with narrowed-down visual features can
possibly lead to predictions with higher accuracy.
