# Applying deep learning to classify pornographic images and videos

arXiv: https://arxiv.org/abs/1511.08899 · HTML: https://mlsanity.com/paper/1511.08899/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2015.

## Built on by (most cited first, 6 of 6)

- [Advancing Content Moderation: Evaluating Large Language Models for Detecting Sensitive Content Across Text, Images, and Videos](https://mlsanity.com/paper/2411.17123/) (2024)
- [On-Device Content Moderation](https://mlsanity.com/paper/2107.11845/) (2021)
- [Validating Multimedia Content Moderation Software via Semantic Fusion](https://mlsanity.com/paper/2305.13623/) (2023)
- [Leveraging Large-scale Multimedia Datasets to Refine Content Moderation Models](https://mlsanity.com/paper/2212.00668/) (2022)
- [Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning](https://mlsanity.com/paper/2406.12736/) (2024)
- [SHAN: Object-Level Privacy Detection via Inference on Scene Heterogeneous Graph](https://mlsanity.com/paper/2403.09172/) (2024)

## Abstract (arXiv)

It is no secret that pornographic material is now a one-click-away from everyone, including children and minors. General social media networks are striving to isolate adult images and videos from normal ones. Intelligent image analysis methods can help to automatically detect and isolate questionable images in media. Unfortunately, these methods require vast experience to design the classifier including one or more of the popular computer vision feature descriptors. We propose to build a classifier based on one of the recently flourishing deep learning techniques. Convolutional neural networks contain many layers for both automatic features extraction and classification. The benefit is an easier system to build (no need for hand-crafting features and classifiers). Additionally, our experiments show that it is even more accurate than the state of the art methods on the most recent benchmark dataset.