13 citations · 17 across the 4 of their papers we have counts for
4 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…
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…
Towards Scalable and Robust Model Versioning
Wenxin Ding, Arjun Nitin Bhagoji, Ben Y. Zhao +1
As the deployment of deep learning models continues to expand across industries, the threat of malicious incursions aimed at gaining access to these deployed models is on the rise.…
Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models
Shawn Shan, Wenxin Ding, Josephine Passananti +3
Data poisoning attacks manipulate training data to introduce unexpected behaviors into machine learning models at training time. For text-to-image generative models with massive tr…