6 papers · 1 filter
CollaFuse: Collaborative Diffusion Models
Simeon Allmendinger, Domenique Zipperling, Lukas Struppek +1
In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic images. However, the application of diffu…
Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models
Dominik Hintersdorf, Lukas Struppek, Kristian Kersting +2
Diffusion models (DMs) produce very detailed and high-quality images. Their power results from extensive training on large amounts of data, usually scraped from the internet withou…
CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AI
Domenique Zipperling, Simeon Allmendinger, Lukas Struppek +1
In the landscape of generative artificial intelligence, diffusion-based models present challenges for socio-technical systems in data requirements and privacy. Traditional approach…
Defending Our Privacy With Backdoors
Dominik Hintersdorf, Lukas Struppek, Daniel Neider +1
The proliferation of large AI models trained on uncurated, often sensitive web-scraped data has raised significant privacy concerns. One of the concerns is that adversaries can ext…
Be Careful What You Smooth For: Label Smoothing Can Be a Privacy Shield but Also a Catalyst for Model Inversion Attacks
Lukas Struppek, Dominik Hintersdorf, Kristian Kersting
Label smoothing -- using softened labels instead of hard ones -- is a widely adopted regularization method for deep learning, showing diverse benefits such as enhanced generalizati…
Balancing Transparency and Risk: The Security and Privacy Risks of Open-Source Machine Learning Models
Dominik Hintersdorf, Lukas Struppek, Kristian Kersting
The field of artificial intelligence (AI) has experienced remarkable progress in recent years, driven by the widespread adoption of open-source machine learning models in both rese…