most citedOn the regularization of Wasserstein GANs

130 citations · 130 across the 1 of their papers we have counts for

collaborators

5 papers

stat.ML2026130 cited

On the regularization of Wasserstein GANs

Henning Petzka, Asja Fischer, Denis Lukovnikov

Since their invention, generative adversarial networks (GANs) have become a popular approach for learning to model a distribution of real (unlabeled) data. Convergence problems dur…

cs.CV2026

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…

cs.CV2026

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…

cs.CR2025

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-…

cs.CR2025

Towards A Correct Usage of Cryptography in Semantic Watermarks for Diffusion Models

Jonas Thietke, Andreas Müller, Denis Lukovnikov +2

Semantic watermarking methods enable the direct integration of watermarks into the generation process of latent diffusion models by only modifying the initial latent noise. One lin…