# On-Device Content Moderation

arXiv: https://arxiv.org/abs/2107.11845 · HTML: https://mlsanity.com/paper/2107.11845/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 25 Jul 2021.

## Builds on (most cited first, 14 of 14)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [The Marginal Value of Adaptive Gradient Methods in Machine Learning](https://mlsanity.com/paper/1705.08292/) (2017)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [Applying deep learning to classify pornographic images and videos](https://mlsanity.com/paper/1511.08899/) (2015)
- [Pornographic Image Recognition via Weighted Multiple Instance Learning](https://mlsanity.com/paper/1902.03771/) (2019)

## Built on by (most cited first, 5 of 5)

- [Leveraging Large-scale Multimedia Datasets to Refine Content Moderation Models](https://mlsanity.com/paper/2212.00668/) (2022)
- [Artificial Intelligence for Web 3.0: A Comprehensive Survey](https://mlsanity.com/paper/2309.09972/) (2023)
- [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)
- [Old Tricks, New Models: How Simple Image Transformations Break Modern AI-based Content Moderation](https://mlsanity.com/paper/2607.28187/) (2026)

## Abstract (arXiv)

With the advent of internet, not safe for work(NSFW) content moderation is a major problem today. Since,smartphones are now part of daily life of billions of people,it becomes even more important to have a solution which coulddetect and suggest user about potential NSFW content present ontheir phone. In this paper we present a novel on-device solutionfor detecting NSFW images. In addition to conventional porno-graphic content moderation, we have also included semi-nudecontent moderation as it is still NSFW in a large demography.We have curated a dataset comprising of three major categories,namely nude, semi-nude and safe images. We have created anensemble of object detector and classifier for filtering of nudeand semi-nude contents. The solution provides unsafe body partannotations along with identification of semi-nude images. Weextensively tested our proposed solution on several public datasetand also on our custom dataset. The model achieves F1 scoreof 0.91 with 95% precision and 88% recall on our customNSFW16k dataset and 0.92 MAP on NPDI dataset. Moreover itachieves average 0.002 false positive rate on a collection of safeimage open datasets.