Showing cs.CVShow all
2 papers · 1 filter
cs.CV2026
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…
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…