Erwin Quiring — accepted AI-conference papers

2026

ClusterMark: Towards Robust Watermarking for Autoregressive Image Generators with Visual Token Clustering

Denis Lukovnikov, Andreas Müller, Erwin Quiring, Asja Fischer

CVPR 2026

In-generation watermarking for latent diffusion models has recently shown high robustness in marking generated images for easier detection and attribution. However, its application to autoregressive (AR) image models is underexplored. Autoregressive models generate images by autoregressively predict

Cited by 0 Source ↗code ↗Scholar ↗

2025

Black-Box Forgery Attacks on Semantic Watermarks for Diffusion Models

Andreas Müller, Denis Lukovnikov, Jonas Thietke, Asja Fischer, Erwin Quiring

CVPR 2025poster

Integrating watermarking into the generation process of latent diffusion models (LDMs) simplifies detection and attribution of generated content. Semantic watermarks, such as Tree-Rings and Gaussian Shading, represent a novel class of watermarking techniques that are easy to implement and highly rob…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗