7 papers
On the Feasibility of Poisoning Text-to-Image AI Models via Adversarial Mislabeling
Stanley Wu, Ronik Bhaskar, Anna Yoo Jeong Ha +3
Today's text-to-image generative models are trained on millions of images sourced from the Internet, each paired with a detailed caption produced by Vision-Language Models (VLMs).…
Somesite I Used To Crawl: Awareness, Agency and Efficacy in Protecting Content Creators From AI Crawlers
Enze Liu, Elisa Luo, Shawn Shan +3
The success of generative AI relies heavily on training on data scraped through extensive crawling of the Internet, a practice that has raised significant copyright, privacy, and e…
Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models
Shawn Shan, Jenna Cryan, Emily Wenger +3
Recent text-to-image diffusion models such as MidJourney and Stable Diffusion threaten to displace many in the professional artist community. In particular, models can learn to mim…
Understanding Implosion in Text-to-Image Generative Models
Wenxin Ding, Cathy Y. Li, Shawn Shan +2
Recent works show that text-to-image generative models are surprisingly vulnerable to a variety of poisoning attacks. Empirical results find that these models can be corrupted by a…
Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?
Anna Yoo Jeong Ha, Josephine Passananti, Ronik Bhaskar +4
The advent of generative AI images has completely disrupted the art world. Distinguishing AI generated images from human art is a challenging problem whose impact is growing over t…
Disrupting Style Mimicry Attacks on Video Imagery
Josephine Passananti, Stanley Wu, Shawn Shan +2
Generative AI models are often used to perform mimicry attacks, where a pretrained model is fine-tuned on a small sample of images to learn to mimic a specific artist of interest.…