A Marketplace for AI-Generated Adult Content and Deepfakes
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Abstract (arXiv)
Generative AI systems increasingly enable the production of highly realistic synthetic media. Civitai, a popular community-driven platform for AI-generated content, operates a monetized feature called Bounties, which allows users to commission the generation of content in exchange for payment. To examine how this mechanism is used and what content it incentivizes, we conduct a longitudinal analysis of all publicly available bounty requests collected over a 14-month period following the platform's launch. We find that the bounty marketplace is dominated by tools that let users steer AI models toward content they were not trained to generate. At the same time, requests for content that is "Not Safe For Work" are widespread and have increased steadily over time, now comprising a majority of all bounties. Participation in bounty creation is uneven, with 20% of requesters accounting for roughly half of requests. Requests for "deepfake" - media depicting identifiable real individuals - exhibit a higher concentration than other types of bounties. A nontrivial subset of these requests involves explicit deepfakes despite platform policies prohibiting such content. These bounties disproportionately target female celebrities, revealing a pronounced gender asymmetry in social harm. Together, these findings show how monetized, community-driven generative AI platforms can produce gendered harms, raising questions about consent, governance, and enforcement.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 45 references that are in the map. First posted on arXiv 14 Jan 2026.
Builds on
11 of 45 references.
References · 11
- 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
- 2021Zero-Shot Text-to-Image Generationcited by 4,276
- 2022On the detection of synthetic images generated by diffusion modelscited by 282
- 2023Art and the science of generative AI: A deeper divecited by 93
- 2024FakeInversion: Learning to Detect Images from Unseen Text-to-Image Models by Inverting Stable Diffusioncited by 61
- 2023Misusing Tools in Large Language Models With Visual Adversarial Examplescited by 40
- 2025Deepfakes on Demand: the rise of accessible non-consensual deepfake image generatorscited by 15
- 2024Characteristics and prevalence of fake social media profiles with AI-generated facescited by 14
- 2024Exploring the Use of Abusive Generative AI Models on Civitaicited by 9
- 2025AI Generated Child Sexual Abuse Material -- What's the Harm?cited by 5
- 2024Civiverse: A Dataset for Analyzing User Engagement with Open-Source Text-to-Image Modelscited by 3
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