# VidHarm: A Clip Based Dataset for Harmful Content Detection

arXiv: https://arxiv.org/abs/2106.08323 · HTML: https://mlsanity.com/paper/2106.08323/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [What Makes Training Multi-Modal Classification Networks Hard?](https://mlsanity.com/paper/1905.12681/) (2019)
- [Are we done with ImageNet?](https://mlsanity.com/paper/2006.07159/) (2020)
- [EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition](https://mlsanity.com/paper/1908.08498/) (2019)
- [Audiovisual SlowFast Networks for Video Recognition](https://mlsanity.com/paper/2001.08740/) (2020)
- [A Comprehensive Study of Deep Video Action Recognition](https://mlsanity.com/paper/2012.06567/) (2020)
- [Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children](https://mlsanity.com/paper/1901.07046/) (2019)

## Built on by (most cited first, 4 of 4)

- [SafeSora: Towards Safety Alignment of Text2Video Generation via a Human Preference Dataset](https://mlsanity.com/paper/2406.14477/) (2024)
- [DisasterInsight: A Building-Centric Benchmark for Evaluating Vision--Language Models in Disaster Response](https://mlsanity.com/paper/2601.18493/) (2026)
- [DeepSVU: Towards In-depth Security-oriented Video Understanding via Unified Physical-world Regularized MoE](https://mlsanity.com/paper/2602.18019/) (2026)
- [Extraction and Summarization of Explicit Video Content using Multi-Modal Deep Learning](https://mlsanity.com/paper/2311.10899/) (2023)

## Abstract (arXiv)

Automatically identifying harmful content in video is an important task with a wide range of applications. However, there is a lack of professionally labeled open datasets available. In this work VidHarm, an open dataset of 3589 video clips from film trailers annotated by professionals, is presented. An analysis of the dataset is performed, revealing among other things the relation between clip and trailer level annotations. Audiovisual models are trained on the dataset and an in-depth study of modeling choices conducted. The results show that performance is greatly improved by combining the visual and audio modality, pre-training on large-scale video recognition datasets, and class balanced sampling. Lastly, biases of the trained models are investigated using discrimination probing. VidHarm is openly available, and further details are available at: https://vidharm.github.io