11 citations · 12 across the 5 of their papers we have counts for
5 papers
BIAS-ID: A Framework for Analyzing Transformation Biases in AI-Generated Image Detectors
Jonas Ricker, Asja Fischer, Erwin Quiring
Given the surge of harmful AI-generated imagery online, reliably distinguishing authentic images from generated ones has become an urgent research topic. While many proposed detect…
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…
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…
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-…
AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images
Jonas Ricker, Dennis Assenmacher, Thorsten Holz +2
Recent advances in the field of generative artificial intelligence (AI) have blurred the lines between authentic and machine-generated content, making it almost impossible for huma…