4 citations · 12 across the 8 of their papers we have counts for
8 papers
Lightweight Model Attribution and Detection of Synthetic Speech via Audio Residual Fingerprints
Matías Pizarro, Mike Laszkiewicz, Dorothea Kolossa +1
As speech generation technologies advance, so do risks of impersonation, misinformation, and spoofing. We present a lightweight, training-free approach for detecting synthetic spee…
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2
Simon Damm, Mike Laszkiewicz, Johannes Lederer +1
Recent advances in multimodal foundation models have set new standards in few-shot anomaly detection. This paper explores whether high-quality visual features alone are sufficient…
Benchmarking the Fairness of Image Upsampling Methods
Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt +2
Recent years have witnessed a rapid development of deep generative models for creating synthetic media, such as images and videos. While the practical applications of these models…
Set-Membership Inference Attacks using Data Watermarking
Mike Laszkiewicz, Denis Lukovnikov, Johannes Lederer +1
In this work, we propose a set-membership inference attack for generative models using deep image watermarking techniques. In particular, we demonstrate how conditional sampling fr…
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…
Marginal Tail-Adaptive Normalizing Flows
Mike Laszkiewicz, Johannes Lederer, Asja Fischer
Learning the tail behavior of a distribution is a notoriously difficult problem. By definition, the number of samples from the tail is small, and deep generative models, such as no…
Copula-Based Normalizing Flows
Mike Laszkiewicz, Johannes Lederer, Asja Fischer
Normalizing flows, which learn a distribution by transforming the data to samples from a Gaussian base distribution, have proven powerful density approximations. But their expressi…
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery
Mike Laszkiewicz, Asja Fischer, Johannes Lederer
Many Machine Learning algorithms are formulated as regularized optimization problems, but their performance hinges on a regularization parameter that needs to be calibrated to each…