The Malicious Technical Ecosystem: Exposing Limitations in Technical Governance of AI-Generated Non-Consensual Intimate Images of Adults

Michelle L. Ding, Harini Suresh
4/24/2025
cs.HCcs.AIcs.CY

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Abstract

In this paper, we adopt a survivor-centered approach to locate and dissect the role of sociotechnical AI governance in preventing AI-Generated Non-Consensual Intimate Images (AIG-NCII) of adults, colloquially known as "deep fake pornography." We identify a "malicious technical ecosystem" or "MTE," comprising of open-source face-swapping models and nearly 200 "nudifying" software programs that allow non-technical users to create AIG-NCII within minutes. Then, using the National Institute of Standards and Technology (NIST) AI 100-4 report as a reflection of current synthetic content governance methods, we show how the current landscape of practices fails to effectively regulate the MTE for adult AIG-NCII, as well as flawed assumptions explaining these gaps.

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Cite this paper

@article{ding2025malicious,
  title  = {The Malicious Technical Ecosystem: Exposing Limitations in Technical Governance of AI-Generated Non-Consensual Intimate Images of Adults},
  author = {Michelle L. Ding and Harini Suresh},
  year   = {2025},
  eprint = {2504.17663},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.17663v1}
}

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