Deepfakes on Demand: the rise of accessible non-consensual deepfake image generators
Will Hawkins, Chris Russell, Brent Mittelstadt

TL;DR
This study reveals a significant rise in accessible deepfake image generators online, highlighting the ease of creating non-consensual images using publicly available models and the urgent need for regulation.
Contribution
It provides an empirical analysis of the proliferation and accessibility of deepfake models, emphasizing the widespread use of low-rank adaptation techniques for easy creation.
Findings
Almost 35,000 deepfake model variants identified
Models downloaded nearly 15 million times since Nov 2022
96% of models target women and include non-consensual content
Abstract
Advances in multimodal machine learning have made text-to-image (T2I) models increasingly accessible and popular. However, T2I models introduce risks such as the generation of non-consensual depictions of identifiable individuals, otherwise known as deepfakes. This paper presents an empirical study exploring the accessibility of deepfake model variants online. Through a metadata analysis of thousands of publicly downloadable model variants on two popular repositories, Hugging Face and Civitai, we demonstrate a huge rise in easily accessible deepfake models. Almost 35,000 examples of publicly downloadable deepfake model variants are identified, primarily hosted on Civitai. These deepfake models have been downloaded almost 15 million times since November 2022, with the models targeting a range of individuals from global celebrities to Instagram users with under 10,000 followers. Both…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Hate Speech and Cyberbullying Detection · Evolutionary Psychology and Human Behavior
Methodstravel james · Diffusion
