Peter Lorenz

ITWM, Fraunhofer Institute for Industrial Mathematics

Fraunhoferplatz 1

Kaiserslautern, Germany

Computer science Ph.D. stipend holder at the Fraunhofer Institute for Industrial Mathematics (ITWM) and Fraunhofer Research Center Machine Learning. I am interested in the intersection of ML and computer security. In particular, robustness and trustworthiness of deep neural networks. My key research interest is the analysis of adversarial examples on DNN.

I am advised by Janis Keuper and supervised by Ullrich Köthe. I am an external Ph.D. student at the University of Heidelberg (2nd best-ranked German university).

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News: I am looking for a research position.

news

Mar 18, 2024	I am happy to announce that I am a reviewer at the CVPR Workshop Robustness of Foundation Models 🎉
Jan 29, 2024	I am accepted for the Oxford Summer School - Representation Learning
Oct 18, 2023	I am happy to announce that I am reviewer at ICASSP on the topics federated / split learning and quantum privacy 😄
Aug 26, 2023	Check out my writeups from the Lakera Gandalf hackathon.
Aug 8, 2023	I am glad to share that I am selected as ICCV WS AROW 2023 reviewer 🥳

selected publications

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},
}

ICCV

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 = {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},
}

NeurIPS

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

Aochuan Chen*, Peter Lorenz*, 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 = {Chen*, Aochuan and Lorenz*, Peter and Yao, Yuguang and Chen, Pin-Yu and Liu, Sijia},
  booktitle = {NeurIPS WS TSRML, Safety ML WS, ICASSP23},
  year = {2022},
}

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},
}

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},
}