publications

2026

1. IJCB

   Foundation Models for Face Presentation Attack Detection: A Unified Linear-Probing Benchmark

   Peter Lorenz, Anjith George, and Sébastien Marcel

   In IEEE International Joint Conference on Biometrics (IJCB) , 2026

   Bib HTML

   @inproceedings{lorenz2026foundationpad,
     title = {Foundation Models for Face Presentation Attack Detection: A Unified Linear-Probing Benchmark},
     author = {Lorenz, Peter and George, Anjith and Marcel, S{\'e}bastien},
     booktitle = {IEEE International Joint Conference on Biometrics (IJCB)},
     year = {2026},
   }

2. Submitted

   Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detection

   Peter Lorenz*, Hatef Otroshi Shahreza*, Asif Hussain Khan*, and 2 more authors

   2026

   Bib HTML

   @article{shahreza2026foundation,
     title = {Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detection},
     author = {Lorenz*, Peter and Shahreza*, Hatef Otroshi and Khan*, Asif Hussain and Komaty, Alain and Marcel, S{\'e}bastien},
     booktitle = {In Submission},
     year = {2026},
   }

2025

1. Eurocrypt

   Navigating the Deep: Signature Extraction on Deep Neural Networks

   Haolin Liu, Adrien Siproudhis, Samuel Experton, and 3 more authors

   In Eurocrypt , 2025

   Bib HTML

   @inproceedings{liu2025navigating,
     title = {Navigating the Deep: Signature Extraction on Deep Neural Networks},
     author = {Liu, Haolin and Siproudhis, Adrien and Experton, Samuel and Lorenz, Peter and Boura, Christina and Peyrin, Thomas},
     booktitle = {Eurocrypt},
     year = {2025},
   }

2024

1. ICML

   Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors

   Peter Lorenz, Mario Fernandez, Jens Mueller, and 1 more author

   In ICML 2024 Workshop on the Next Generation of AI Safety , 2024

   Bib HTML

   @inproceedings{lorenz2024deciphering,
     title = {Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors},
     author = {Lorenz, Peter and Fernandez, Mario and Mueller, Jens and Koethe, Ullrich},
     booktitle = {ICML 2024 Workshop on the Next Generation of AI Safety},
     year = {2024},
     url = {https://arxiv.org/pdf/2406.15104},
   }

2. IJCNN

   Adversarial Examples are Misaligned in Diffusion Model Manifolds

   Peter Lorenz, Ricard Durall, and Janis Keuper

   In IJCNN , 2024

   Bib HTML

   @inproceedings{lorenz2024manifold,
     title = {Adversarial Examples are Misaligned in Diffusion Model Manifolds},
     author = {Lorenz, Peter and Durall, Ricard and Keuper, Janis},
     booktitle = {IJCNN},
     year = {2024},
     url = {https://arxiv.org/pdf/2401.06637.pdf},
   }

2023

1. ICCV

   [Withdrawn] Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality

   Peter Lorenz, Ricard Durall, and Janis Keuper

   In ICCV Workshop and Challenge on DeepFake Analysis and Detection , 2023

   Bib HTML

   @inproceedings{lorenz2023detecting,
     title = {[Withdrawn] Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality},
     author = {Lorenz, Peter and Durall, Ricard and Keuper, Janis},
     booktitle = {ICCV Workshop and Challenge on DeepFake Analysis and Detection},
     year = {2023},
     url = {https://arxiv.org/pdf/2307.02347.pdf},
   }

2. VISAPP

   Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection

   Peter Lorenz, Margret Keuper, and  Janis Keuper

   In VISAPP , 2023

   Bib HTML

   @inproceedings{multilid,
     title = {Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection},
     author = {Lorenz, Peter and Keuper, Margret and and Janis Keuper},
     booktitle = {VISAPP},
     year = {2023},
     url = {https://arxiv.org/pdf/2212.06776.pdf},
   }

2022

1. NeurIPS

   Visual Prompting for Adversarial Robustness (top 3% @ ICASSP23)

   Peter Lorenz*, Aochuan Chen*, Yuguang Yao, and 2 more authors

   In NeurIPS WS TSRML, Safety ML WS, ICASSP23 , 2022

   Bib HTML

   @inproceedings{prompting,
     title = {Visual Prompting for Adversarial Robustness (top 3% @ ICASSP23)},
     author = {Lorenz*, Peter and Chen*, Aochuan and Yao, Yuguang and Chen, Pin-Yu and Liu, Sijia},
     booktitle = {NeurIPS WS TSRML, Safety ML WS, ICASSP23},
     year = {2022},
     url = {https://arxiv.org/pdf/2210.06284.pdf},
   }

2. AAAI

   Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?

   Peter Lorenz, Dominik Strassel, Margret Keuper, and 1 more author

   In The AAAI-22 Workshop on Adversarial Machine Learning and Beyond , 2022

   Bib HTML

   @inproceedings{lorenz2022is,
     title = {Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?},
     author = {Lorenz, Peter and Strassel, Dominik and Keuper, Margret and Keuper, Janis},
     booktitle = {The AAAI-22 Workshop on Adversarial Machine Learning and Beyond},
     year = {2022},
     url = {https://openreview.net/forum?id=aLB3FaqoMBs},
   }

2021

1. ICML

   Detecting AutoAttack Perturbations in the Frequency Domain

   Peter Lorenz, Paula Harder, Dominik Straßel, and 2 more authors

   In ICML 2021 Workshop on Adversarial Machine Learning , 2021

   Bib HTML

   @inproceedings{lorenz2021detecting,
     title = {Detecting AutoAttack Perturbations in the Frequency Domain},
     author = {Lorenz, Peter and Harder, Paula and Stra{\ss}el, Dominik and Keuper, Margret and Keuper, Janis},
     booktitle = {ICML 2021 Workshop on Adversarial Machine Learning},
     year = {2021},
     url = {https://openreview.net/forum?id=8uWOTxbwo-Z},
   }