25 citations · 46 across the 12 of their papers we have counts for
12 papers
Training-Free Reconstruction-Based AI-Generated Image Detectors Are Inherently Vulnerable to Adversarial Examples
Roman Demchenko, Jonas Ricker, Asja Fischer
The impressive visual quality and ubiquity of AI-generated images call for reliable and robust detection methods. Reconstruction-based detectors have emerged as a promising directi…
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…
SAMSEM -- A Generic and Scalable Approach for IC Metal Line Segmentation
Christian Gehrmann, Jonas Ricker, Simon Damm +5
In light of globalized hardware supply chains, the assurance of hardware components has gained significant interest, particularly in cryptographic applications and high-stakes scen…
PRADA: Probability-Ratio-Based Attribution and Detection of Autoregressive-Generated Images
Simon Damm, Jonas Ricker, Henning Petzka +1
Autoregressive (AR) image generation has recently emerged as a powerful paradigm for image synthesis. Leveraging the generation principle of large language models, they allow for e…
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…
"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…
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…
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…
AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error
Jonas Ricker, Denis Lukovnikov, Asja Fischer
With recent text-to-image models, anyone can generate deceptively realistic images with arbitrary contents, fueling the growing threat of visual disinformation. A key enabler for g…
A Representative Study on Human Detection of Artificially Generated Media Across Countries
Joel Frank, Franziska Herbert, Jonas Ricker +5
AI-generated media has become a threat to our digital society as we know it. These forgeries can be created automatically and on a large scale based on publicly available technolog…
Single-Model Attribution of Generative Models Through Final-Layer Inversion
Mike Laszkiewicz, Jonas Ricker, Johannes Lederer +1
Recent breakthroughs in generative modeling have sparked interest in practical single-model attribution. Such methods predict whether a sample was generated by a specific generator…
Towards the Detection of Diffusion Model Deepfakes
Jonas Ricker, Simon Damm, Thorsten Holz +1
In the course of the past few years, diffusion models (DMs) have reached an unprecedented level of visual quality. However, relatively little attention has been paid to the detecti…