8 papers
Adversarial Robustness of AI-Generated Image Detectors in the Real World
Sina Mavali, Jonas Ricker, David Pape +2
The rapid advancement of Generative Artificial Intelligence (GenAI) capabilities is accompanied by a concerning rise in its misuse. In particular the generation of credible misinfo…
On the Robustness of Watermarking for Autoregressive Image Generation
Andreas Müller, Denis Lukovnikov, Shingo Kodama +5
The proliferation of autoregressive (AR) image generators demands reliable detection and attribution of their outputs to mitigate misinformation, and to filter synthetic images fro…
ClusterMark: Towards Robust Watermarking for Autoregressive Image Generators with Visual Token Clustering
Denis Lukovnikov, Andreas Müller, Erwin Quiring +1
In-generation watermarking for latent diffusion models has recently shown high robustness in marking generated images for easier detection and attribution. However, its application…
"That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation
Sandra Höltervennhoff, Jonas Ricker, Maike M. Raphael +6
As generative AI is increasingly contributing to the spread of deceptively realistic misinformation, lawmakers have introduced regulations requiring the disclosure of AI-generated…
RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors
Hicham Eddoubi, Jonas Ricker, Federico Cocchi +7
AI-generated images have reached a quality level at which humans are incapable of reliably distinguishing them from real images. To counteract the inherent risk of fraud and disinf…
Black-Box Forgery Attacks on Semantic Watermarks for Diffusion Models
Andreas Müller, Denis Lukovnikov, Jonas Thietke +2
Integrating watermarking into the generation process of latent diffusion models (LDMs) simplifies detection and attribution of generated content. Semantic watermarks, such as Tree-…