# State-of-the-Art in Nudity Classification: A Comparative Analysis

arXiv: https://arxiv.org/abs/2312.16338 · HTML: https://mlsanity.com/paper/2312.16338/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 26 Dec 2023.

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

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [On-Device Content Moderation](https://mlsanity.com/paper/2107.11845/) (2021)
- [Smart Content Recognition from Images Using a Mixture of Convolutional Neural Networks](https://mlsanity.com/paper/1612.09506/) (2016)
- [A Multimodal CNN-based Tool to Censure Inappropriate Video Scenes](https://mlsanity.com/paper/1911.03974/) (2019)
- [Deep Architectures for Content Moderation and Movie Content Rating](https://mlsanity.com/paper/2212.04533/) (2022)

## Built on by (most cited first, 3 of 3)

- [Ethical-Lens: Curbing Malicious Usages of Open-Source Text-to-Image Models](https://mlsanity.com/paper/2404.12104/) (2024)
- [Sensitive Image Classification by Vision Transformers](https://mlsanity.com/paper/2412.16446/) (2024)
- [SenBen: Sensitive Scene Graphs for Explainable Content Moderation](https://mlsanity.com/paper/2604.08819/) (2026)

## Abstract (arXiv)

This paper presents a comparative analysis of existing nudity classification techniques for classifying images based on the presence of nudity, with a focus on their application in content moderation. The evaluation focuses on CNN-based models, vision transformer, and popular open-source safety checkers from Stable Diffusion and Large-scale Artificial Intelligence Open Network (LAION). The study identifies the limitations of current evaluation datasets and highlights the need for more diverse and challenging datasets. The paper discusses the potential implications of these findings for developing more accurate and effective image classification systems on online platforms. Overall, the study emphasizes the importance of continually improving image classification models to ensure the safety and well-being of platform users. The project page, including the demonstrations and results is publicly available at https://github.com/fcakyon/content-moderation-deep-learning.