Peter Lorenz

NTU

Machine Learning and Cryptanalysis

I am currently a postdoctoral researcher at Nanyang Technological University (NTU), Singapore, ranked 2nd worldwide in AI research. My current research conducts adversarial machine learning and cryptanalysis, e.g. model stealing or new adversarial attacks.

Previously, I earned my PhD with magna cum laude from Heidelberg University, Germany, where 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). My key research interest was the analysis of adversarial examples on DNN. In particular, deep neural networks’ robustness and trustworthiness.

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

Aug 31, 2024	Analysis of Adversarial Examples (PhD Thesis)
Jun 06, 2024	Habits
Jun 06, 2024	How to write a good scientific review?

selected publications

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},
}

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},
}