mlsanityData to 7 Oct 2026

Analyzing the AI Nudification Application Ecosystem

arXiv 2411.09751 ↗First posted 14 Nov 2024cs.HC cs.CVcited by 9

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Abstract (arXiv)

Given a source image of a clothed person (an image subject), AI-based nudification applications can produce nude (undressed) images of that person. Moreover, not only do such applications exist, but there is ample evidence of the use of such applications in the real world and without the consent of an image subject. Still, despite the growing awareness of the existence of such applications and their potential to violate the rights of image subjects and cause downstream harms, there has been no systematic study of the nudification application ecosystem across multiple applications. We conduct such a study here, focusing on 20 popular and easy-to-find nudification websites. We study the positioning of these web applications (e.g., finding that most sites explicitly target the nudification of women, not all people), the features that they advertise (e.g., ranging from undressing-in-place to the rendering of image subjects in sexual positions, as well as differing user-privacy options), and their underlying monetization infrastructure (e.g., credit cards and cryptocurrencies). We believe this work will empower future, data-informed conversations -- within the scientific, technical, and policy communities -- on how to better protect individuals' rights and minimize harm in the face of modern (and future) AI-based nudification applications. Content warning: This paper includes descriptions of web applications that can be used to create synthetic non-consensual explicit AI-created imagery (SNEACI). This paper also includes an artistic rendering of a user interface for such an application.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 9 of its 69 references that are in the map. First posted on arXiv 14 Nov 2024.

Builds on

9 of 69 references.

References · 9

  1. 2018Generative Image Inpainting with Contextual Attentioncited by 578
  2. 2017On Face Segmentation, Face Swapping, and Face Perceptioncited by 71
  3. 2023A Resilient and Accessible Distribution-Preserving Watermark for Large Language Modelscited by 47
  4. 2024Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Datacited by 20
  5. 2019False News On Social Media: A Data-Driven Surveycited by 9
  6. 2024Non-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes, and Knowledge in 10 Countriescited by 8
  7. 2024"Violation of my body:" Perceptions of AI-generated non-consensual (intimate) imagerycited by 5
  8. 2021Protection or punishment? relating the design space of parental control apps and perceptions about them to support parenting for online safetycited by 3
  9. 2024Understanding Help-Seeking and Help-Giving on Social Media for Image-Based Sexual Abusecited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 7.

9 papers.