# Non-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes, and Knowledge in 10 Countries

arXiv: https://arxiv.org/abs/2402.01721 · HTML: https://mlsanity.com/paper/2402.01721/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 83 references that are in the map.
- First posted on arXiv 26 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [Deepfake Video Detection Using Convolutional Vision Transformer](https://mlsanity.com/paper/2102.11126/) (2021)
- [Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds](https://mlsanity.com/paper/2105.06496/) (2021)
- [An Examination of Fairness of AI Models for Deepfake Detection](https://mlsanity.com/paper/2105.00558/) (2021)
- [GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection](https://mlsanity.com/paper/2207.10246/) (2022)
- [Deepfake Videos in the Wild: Analysis and Detection](https://mlsanity.com/paper/2103.04263/) (2021)
- [Warning: Humans Cannot Reliably Detect Speech Deepfakes](https://mlsanity.com/paper/2301.07829/) (2023)
- [Testing Human Ability To Detect Deepfake Images of Human Faces](https://mlsanity.com/paper/2212.05056/) (2022)
- [Deepfake detection: humans vs. machines](https://mlsanity.com/paper/2009.03155/) (2020)
- [Are Deepfakes Concerning? Analyzing Conversations of Deepfakes on Reddit and Exploring Societal Implications](https://mlsanity.com/paper/2203.15044/) (2022)
- [Transdisciplinary AI Observatory -- Retrospective Analyses and Future-Oriented Contradistinctions](https://mlsanity.com/paper/2012.02592/) (2020)
- [Online Harassment in Majority Contexts: Examining Harms and Remedies across Countries](https://mlsanity.com/paper/2301.11715/) (2023)

## Built on by (most cited first, 8 of 8)

- [Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024](https://mlsanity.com/paper/2503.02857/) (2025)
- [Analyzing the AI Nudification Application Ecosystem](https://mlsanity.com/paper/2411.09751/) (2024)
- ["Unlimited Realm of Exploration and Experimentation": Methods and Motivations of AI-Generated Sexual Content Creators](https://mlsanity.com/paper/2601.21028/) (2026)
- [When Testing AI Tests Us: Safeguarding Mental Health on the Digital Frontlines](https://mlsanity.com/paper/2504.20910/) (2025)
- [When Understanding Becomes a Risk: Authenticity and Safety Risks in the Emerging Image Generation Paradigm](https://mlsanity.com/paper/2603.24079/) (2026)
- [Reverse Personalization](https://mlsanity.com/paper/2512.22984/) (2025)
- [Characterizing Resource Sharing Practices on Underground Internet Forum Synthetic Non-Consensual Intimate Image Content Creation Communities](https://mlsanity.com/paper/2604.12190/) (2026)
- [VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection](https://mlsanity.com/paper/2607.06254/) (2026)

## Abstract (arXiv)

Deepfake technologies have become ubiquitous, "democratizing" the ability to manipulate photos and videos. One popular use of deepfake technology is the creation of sexually explicit content, which can then be posted and shared widely on the internet. Drawing on a survey of over 16,000 respondents in 10 different countries, this article examines attitudes and behaviors related to "deepfake pornography" as a specific form of non-consensual synthetic intimate imagery (NSII). Our study found that deepfake pornography behaviors were considered harmful by respondents, despite nascent societal awareness. Regarding the prevalence of deepfake porn victimization and perpetration, 2.2% of all respondents indicated personal victimization, and 1.8% all of respondents indicated perpetration behaviors. Respondents from countries with specific legislation still reported perpetration and victimization experiences, suggesting NSII laws are inadequate to deter perpetration. Approaches to prevent and reduce harms may include digital literacy education, as well as enforced platform policies, practices, and tools which better detect, prevent, and respond to NSII content.