# A Multimodal CNN-based Tool to Censure Inappropriate Video Scenes

arXiv: https://arxiv.org/abs/1911.03974 · HTML: https://mlsanity.com/paper/1911.03974/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2019.

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

- [Dynamic Content Moderation in Livestreams: Combining Supervised Classification with MLLM-Boosted Similarity Matching](https://mlsanity.com/paper/2512.03553/) (2025)
- [State-of-the-Art in Nudity Classification: A Comparative Analysis](https://mlsanity.com/paper/2312.16338/) (2023)
- [Deep Architectures for Content Moderation and Movie Content Rating](https://mlsanity.com/paper/2212.04533/) (2022)
- [Optimizing GPT for Video Understanding: Zero-Shot Performance and Prompt Engineering](https://mlsanity.com/paper/2502.09573/) (2025)

## Abstract (arXiv)

Due to the extensive use of video-sharing platforms and services for their storage, the amount of such media on the internet has become massive. This volume of data makes it difficult to control the kind of content that may be present in such video files. One of the main concerns regarding the video content is if it has an inappropriate subject matter, such as nudity, violence, or other potentially disturbing content. More than telling if a video is either appropriate or inappropriate, it is also important to identify which parts of it contain such content, for preserving parts that would be discarded in a simple broad analysis. In this work, we present a multimodal~(using audio and image features) architecture based on Convolutional Neural Networks (CNNs) for detecting inappropriate scenes in video files. In the task of classifying video files, our model achieved 98.95% and 98.94% of F1-score for the appropriate and inappropriate classes, respectively. We also present a censoring tool that automatically censors inappropriate segments of a video file.