3 citations · 7 across the 9 of their papers we have counts for
9 papers
Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detection
Hatef Otroshi Shahreza, Asif Hussain Khan, Peter Lorenz +2
Face recognition systems are increasingly deployed in security-critical applications, yet they remain vulnerable to presentation and morph attacks. Presentation attack detection (P…
LoRA-based Adaptation Alone Is Not Enough: Understanding the Limits of Foundation Models for Face Presentation Attack Detection
Peter Lorenz, Anjith George, Marcel Sébastien
Face presentation attack detection (PAD) aims to reliably detect a wide range of presentation attacks. While PAD methods achieve strong performance within individual datasets, thei…
Foundation Models for Face Presentation Attack Detection: A Unified Linear-Probing Benchmark
Peter Lorenz, Anjith George, Sébastien Marcel
Face presentation attack detection (PAD) remains challenging under cross-dataset evaluation, where domain shift degrades models trained on a single dataset. The scarcity of large-s…
Adversarial Examples are Misaligned in Diffusion Model Manifolds
Peter Lorenz, Ricard Durall, Janis Keuper
In recent years, diffusion models (DMs) have drawn significant attention for their success in approximating data distributions, yielding state-of-the-art generative results. Nevert…
Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality
Peter Lorenz, Ricard Durall, Janis Keuper
Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for ma…
Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection
Peter Lorenz, Margret Keuper, Janis Keuper
Convolutional neural networks (CNN) define the state-of-the-art solution on many perceptual tasks. However, current CNN approaches largely remain vulnerable against adversarial per…
Visual Prompting for Adversarial Robustness
Aochuan Chen, Peter Lorenz, Yuguang Yao +2
In this work, we leverage visual prompting (VP) to improve adversarial robustness of a fixed, pre-trained model at testing time. Compared to conventional adversarial defenses, VP a…
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?
Peter Lorenz, Dominik Strassel, Margret Keuper +1
Recently, RobustBench (Croce et al. 2020) has become a widely recognized benchmark for the adversarial robustness of image classification networks. In its most commonly reported su…
Detecting AutoAttack Perturbations in the Frequency Domain
Peter Lorenz, Paula Harder, Dominik Strassel +2
Recently, adversarial attacks on image classification networks by the AutoAttack (Croce and Hein, 2020b) framework have drawn a lot of attention. While AutoAttack has shown a very…