# From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan

arXiv: https://arxiv.org/abs/2606.27234 · HTML: https://mlsanity.com/paper/2606.27234/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Builds on (most cited first, 18 of 18)

- [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)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models](https://mlsanity.com/paper/2305.13873/) (2023)
- [DeepFaceLab: Integrated, flexible and extensible face-swapping framework](https://mlsanity.com/paper/2005.05535/) (2020)
- [T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Privacy in Image Generation](https://mlsanity.com/paper/2501.12612/) (2025)
- [Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?](https://mlsanity.com/paper/2402.03214/) (2024)
- [Deepfakes on Demand: the rise of accessible non-consensual deepfake image generators](https://mlsanity.com/paper/2505.03859/) (2025)
- [Towards Understanding Unsafe Video Generation](https://mlsanity.com/paper/2407.12581/) (2024)
- [Analyzing the AI Nudification Application Ecosystem](https://mlsanity.com/paper/2411.09751/) (2024)
- [Training-Free Safe Text Embedding Guidance for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2510.24012/) (2025)
- [Characterizing the MrDeepFakes Sexual Deepfake Marketplace](https://mlsanity.com/paper/2410.11100/) (2024)
- [Video Deepfake Abuse: How Company Choices Predictably Shape Misuse Patterns](https://mlsanity.com/paper/2512.11815/) (2025)
- [When Image Generation Goes Wrong: A Safety Analysis of Stable Diffusion Models](https://mlsanity.com/paper/2411.15516/) (2024)
- ["Unlimited Realm of Exploration and Experimentation": Methods and Motivations of AI-Generated Sexual Content Creators](https://mlsanity.com/paper/2601.21028/) (2026)
- [U-Sticker: A Large-Scale Multi-Domain User Sticker Dataset for Retrieval and Personalization](https://mlsanity.com/paper/2502.19108/) (2025)

## Abstract (arXiv)

AI nudification uses generative models to create synthetic non-consensual sexually explicit imagery (SNEACI) of real individuals. Prior work has examined dedicated nudification platforms and model repositories, finding that most targets are female celebrities. However, the anonymous content community, where SNEACI is actively requested, generated, and exchanged, remains unexplored. In this work, we present a large-scale study of AI nudification in the wild, identifying 24,105 SNEACI items. We find a significant shift in target demographics: non-celebrity individuals now account for 55.8% of targets, compared to only 4.7% in prior studies, indicating that AI nudification has expanded from targeting public figures to increasingly harming individuals within users' own social circles. Meanwhile, open-source models dominate production, with Stable Diffusion family generating 42.7% of images and Wan generating 66.5% of videos, all driven by thousands of shared fine-tuned models and accessible tutorials. Yet the ecosystem runs on a small cohort of active producers, with the most prolific producing 780 items, drives community engagement, shapes target demographics, and disseminates technical knowledge that lowers barriers for new producers. Our work provides an empirical understanding of how AI nudification operates in the wild, revealing the mechanisms that sustain this ecosystem and highlighting the urgent need for interventions in platform governance, technical safeguards, and affected individual protection.