1 citations · 2 across the 5 of their papers we have counts for
5 papers
An Empirical Analysis of AI Slop in Music Streaming
Stanley Wu, Josephine Passananti, Viresh Mittal +3
Generative AI models lower the bar for content creation, making it easy for any user to create professional-looking images, text and music with minimal effort. This has enabled a n…
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).…
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.…
A Response to Glaze Purification via IMPRESS
Shawn Shan, Stanley Wu, Haitao Zheng +1
Recent work proposed a new mechanism to remove protective perturbation added by Glaze in order to again enable mimicry of art styles from images protected by Glaze. Despite promisi…
TMI! Finetuned Models Leak Private Information from their Pretraining Data
John Abascal, Stanley Wu, Alina Oprea +1
Transfer learning has become an increasingly popular technique in machine learning as a way to leverage a pretrained model trained for one task to assist with building a finetuned…