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, deep neural networks’ robustness and trustworthiness. My key research interest is the analysis of adversarial examples on DNN.

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

I also had the opportunity to intern with Prof. Sijia Liu (MSU & MIT-IBM) and Pin-Yu Chen (MIT-IBM), focusing on adversarial machine learning. Our “Visual Prompting for Adversarial Robustness” paper achieved recognition within the top 3% at the ICASSP conference.

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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.

latest posts

Jun 06, 2024	Habits
Jun 06, 2024	How to write a good scientific review?
May 14, 2024	Google Gemini updates: Flash 1.5, Gemma 2 and Project Astra

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},
  url = {https://arxiv.org/pdf/2401.06637.pdf},
}

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},
  url = {https://arxiv.org/pdf/2210.06284.pdf},
}

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},
}