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Mario Fritz – CISPA Helmholtz Center for Information Security

Mario Fritz – CISPA Helmholtz Center for Information Security

Prof. Dr. Mario Fritz


Faculty
CISPA Helmholtz Center for Information Security

Professor
Saarland University

Fellow
European Laboratory for Learning and Intelligent Systems (ELLIS)


Google Scholar 

Semantic Scholar


We are looking for PhD students and Post-Docs! Please get in touch.

My group is working on Trustworthy Information Processing with a focus on the intersection of AI & Machine Learning with Security & Privacy.


Recent projects and initiatives related to trustworthy AI/ML, health, privacy:

  • Coordinator and PI: European Lighthouse on Secure and Safe AI (ELSA)
  • Leading Scientist: Helmholtz Medical Security, Privacy, and AI Research Center (HMSP)
  • Coordinator and PI: Trustworthy Federated Data Analytics Project (TFDA)
  • Coordinator and PI: Protecting Genetic Data with Synthetic Cohorts from Deep Generative Models (PRO-GENE-GEN)
  • PI: Integrated Early Warning System for Local Recognition, Prevention, and Control for Epidemic Outbreaks (LOKI)
  • Partner-PI: The German Human Genome-Phenome Archive (GHGA)
  • Member of working group in “Forum Gesundheit” of BMBF: “AG Nutzbarmachung digitaler Daten für KI-Entwicklungen in der Gesundheitsforschung”

Recent work on DeepFake detecting, misinformation, attribution,and responsible disclosure:

  • Usenix’23: UnGANable: Defending Against GAN-based Face Manipulation
  • ArXiv’22: Fact-Saboteurs: A Taxonomy of Evidence Manipulation Attacks against Fact-Verification Systems
  • CVPR’22: Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources
  • ICLR’22: Responsible Disclosure of Generative Models Using Scalable Fingerprinting
  • ICCV’21: Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data
  • S&P’21: Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding
  • IJCAI’21: Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis
  • CVPR’21: Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs
  • ICCV’19: Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints

Recent publications:

  • Usenix’23: UnGANable: Defending Against GAN-based Face Manipulation
  • NeurIPS’22: Private Set Generation with Discriminative Information
  • ICML’22: ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training
  • CVPR’22: Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources
  • CVPR’22: B-cos Networks: Alignment is All We Need for Interpretability
  • CHIL’22: Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data
  • ICLR’22: Responsible Disclosure of Generative Models Using Scalable Fingerprinting
  • ICLR’22: RelaxLoss: Defending Membership Inference Attacks without Losing Utility
  • Usenix’22: ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models
  • PETS’22: Understanding Utility and Privacy of Demographic Data in Education Technology by Causal Analysis and Adversarial-Censoring
  • IJCV’21: Semantic Bottlenecks: Quantifying and Improving Inspectability of Deep Representations
  • CCS-W’21: Differential Privacy Defenses and Sampling Attacks for Membership Inference
  • CCS-W’21: “What’s in the box?!”: Deflecting Adversarial Attacks by Randomly Deploying Adversarially-Disjoint Models
  • PKDD-W’21: IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis
  • PKDD-W’21: SampleFix: Learning to Generate Functionally Diverse Fixes
  • ICCV’21: Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data
  • ICCV’21: Dual Contrastive Loss and Attention for GANs
  • EXCLI’21: Privacy Considerations for Sharing Genomics Data
  • IJCAI’21: Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis
  • S&P’21: Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding
  • CVPR’21: Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs
  • CVPR’21: Convolutional Dynamic Alignment Networks for Interpretable Classifications
  • CVPR’21: Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers
  • CVPR-W’21: MLCapsule: Guarded Offline Deployment of Machine Learning as a Service
  • CVPR-W’21: InfoScrub: Towards Attribute Privacy by Targeted Obfuscation
  • WACV’21: Future Moment Assessment for Action Query

News, talks, events:

  • Talk at Deutscher EDV Gerichtstag
  • Talk at AI, Neuroscience and Hardware: From Neural to Artificial Systems and Back Again
  • Scientific Advisory Board: Bosch AIShield
  • Steering Board: Helmholtz.AI
  • Recent program committees: ICML’21, NeurIPS’21, S&P’22, EuroS&P’22, CVPR’22 (AC); CCS’22
  • Runner-up Inria/CNIL Privacy Protection Prize 2020
    S&P’20 paper: “Automatically Detecting Bystanders in Photos to Reduce Privacy Risks”
  • Co-Organizers of ICLR’21 Workshop on “Synthetic Data Generation – Quality, Privacy, Bias”
  • Co-Organizers of CVPR’21 Workshop on “QuoVadis: Interdisciplinary, Socio-Technical Workshop on the Future of Computer Vision and Pattern Recognition (QuoVadis-CVPR)”
  • Co-Organizers of CVPR’21 Workshop on “Causality in Vision”
  • Founding member of Saarbrücken Artificial Intelligence & Machine Learning (SAM) unit of the European Laboratory of Learning and Intelligent Systems (ELLIS)
  • Lecturer at Digital CISPA Summer School 2020
  • Co-Organizer of Third International Workshop on The Bright and Dark Sides of Computer Vision: Challenges and Opportunities for Privacy and Security (CV-COPS) at ECCV 2020
  • Co-Organizer: 4. ACM Symposium on Computer Science in Cars: Future Challenges in Artificial Intelligence & Security for Autonomous Vehicles CSCS’20
  • Keynote at Workshop Machine Learning for Cybersecurity, ECMLPKDD’19
  • Talk at Cyber Defense Campus (CYD) Conference on Artificial Intelligence in Defence and Security
  • Co-Organizer of Second International Workshop on The Bright and Dark Sides of Computer Vision: Challenges and Opportunities for Privacy and Security (CV-COPS) at CVPR 2019
  • Co-Organizer: 3. ACM Symposium on Computer Science in Cars: Future Challenges in Artificial Intelligence & Security for Autonomous Vehicles CSCS’19
  • Leading scientist at new Helmholtz Medical Security and Privacy Research Center
  • Member of ACM Technical Policy Committee Europe
  • Mateusz Malinowski received the DAGM MVTec dissertation award as well as the Dr.-Eduard-Martin award for his PhD
  • Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

2011 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Show all

2013

Incollections

A Category-Level 3D Object Dataset: Putting the Kinect to Work

Allison Janoch; Sergey Karayev; Yangqing Jia; Jonathan T. Barron; Mario Fritz; Kate Saenko; Trevor Darrell

A Category-Level 3D Object Dataset: Putting the Kinect to Work Incollection

In: Fossati, Andrea; Gall, Juergen; Grabner, Helmut; Ren, Xiaofeng; Konolige, Kurt (Ed.): Consumer Depth Cameras for Computer Vision, Springer London, 2013.

Links | BibTeX | Tags: 2013

@incollection{b3do,
title = {A Category-Level 3D Object Dataset: Putting the Kinect to Work},
author = {Allison Janoch and Sergey Karayev and Yangqing Jia and Jonathan T. Barron and Mario Fritz and Kate Saenko and Trevor Darrell},
editor = {Andrea Fossati and Juergen Gall and Helmut Grabner and Xiaofeng Ren and Kurt Konolige},
url = {http://dx.doi.org/10.1007/978-1-4471-4640-7_8},
year = {2013},
date = {2013-08-01},
booktitle = {Consumer Depth Cameras for Computer Vision},
publisher = {Springer London},
series = {Advances in Computer Vision and Pattern Recognition},
keywords = {2013},
pubstate = {published},
tppubtype = {incollection}
}

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  • http://dx.doi.org/10.1007/978-1-4471-4640-7_8

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Inproceedings

Sequential Bayesian Model Update under Structured Scene Prior for Semantic Road Scenes Labeling

Evgeny Levinkov; Mario Fritz

Sequential Bayesian Model Update under Structured Scene Prior for Semantic Road Scenes Labeling Inproceedings

In: IEEE International Conference on Computer Vision (ICCV), 2013.

Links | BibTeX | Tags: 2013

@inproceedings{levinkov13iccv,
title = {Sequential Bayesian Model Update under Structured Scene Prior for Semantic Road Scenes Labeling},
author = {Evgeny Levinkov and Mario Fritz},
url = {https://cispa.saarland/group/fritz/wp-content/blogs.dir/13/files/2020/06/levinkov13iccv.pdf
http://www.d2.mpi-inf.mpg.de/sequential-bayesian-update},
year = {2013},
date = {2013-12-03},
booktitle = {IEEE International Conference on Computer Vision (ICCV)},
keywords = {2013},
pubstate = {published},
tppubtype = {inproceedings}
}

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  • https://cispa.saarland/group/fritz/wp-content/blogs.dir/13/files/2020/06/levinko[...]
  • http://www.d2.mpi-inf.mpg.de/sequential-bayesian-update

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Learning Smooth Pooling Regions for Visual Recognition

Mateusz Malinowski; Mario Fritz

Learning Smooth Pooling Regions for Visual Recognition Inproceedings

In: British Machine Vision Conference (BMVC), 2013.

Links | BibTeX | Tags: 2013

@inproceedings{mateusz13bmvc,
title = {Learning Smooth Pooling Regions for Visual Recognition},
author = {Mateusz Malinowski and Mario Fritz},
url = {https://cispa.saarland/group/fritz/wp-content/blogs.dir/13/files/2020/06/bmvc13_main.pdf},
year = {2013},
date = {2013-09-09},
booktitle = {British Machine Vision Conference (BMVC)},
keywords = {2013},
pubstate = {published},
tppubtype = {inproceedings}
}

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  • https://cispa.saarland/group/fritz/wp-content/blogs.dir/13/files/2020/06/bmvc13_[...]

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Dynamic Feature Selection for Classification on a Budget

Sergey Karayev; Mario Fritz; Trevor Darrell

Dynamic Feature Selection for Classification on a Budget Inproceedings

In: ICML Workshop on Prediction with Sequential Models, 2013.

Links | BibTeX | Tags: 2013

@inproceedings{karayev13icmlw,
title = {Dynamic Feature Selection for Classification on a Budget},
author = {Sergey Karayev and Mario Fritz and Trevor Darrell},
url = {https://cispa.saarland/group/fritz/wp-content/blogs.dir/13/files/2020/06/icmlw_2013_dynamic_feature_selection.pdf},
year = {2013},
date = {2013-07-01},
booktitle = {ICML Workshop on Prediction with Sequential Models},
keywords = {2013},
pubstate = {published},
tppubtype = {inproceedings}
}

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  • https://cispa.saarland/group/fritz/wp-content/blogs.dir/13/files/2020/06/icmlw_2[...]

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Masters Theses

 Scene Segmentation in Adverse Vision Conditions

Evgeny Levinkov

Scene Segmentation in Adverse Vision Conditions Masters Thesis

Saarland University, 2013.

Links | BibTeX | Tags: 2013

@mastersthesis{levinkov13master,
title = { Scene Segmentation in Adverse Vision Conditions},
author = {Evgeny Levinkov},
url = {https://cispa.saarland/group/fritz/wp-content/blogs.dir/13/files/2020/06/Thesis.pdf},
year = {2013},
date = {2013-02-28},
school = {Saarland University},
keywords = {2013},
pubstate = {published},
tppubtype = {mastersthesis}
}

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  • https://cispa.saarland/group/fritz/wp-content/blogs.dir/13/files/2020/06/Thesis.[...]

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Multi-Scale Feature Learning for Material Recognition

Wenbin Li

Multi-Scale Feature Learning for Material Recognition Masters Thesis

Saarland University, 2013.

BibTeX | Tags: 2013

@mastersthesis{wenbin13multiscale,
title = {Multi-Scale Feature Learning for Material Recognition},
author = {Wenbin Li},
year = {2013},
date = {2013-01-31},
school = {Saarland University},
keywords = {2013},
pubstate = {published},
tppubtype = {mastersthesis}
}

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Technical Reports

Learnable Pooling Regions for Image Classification

Mateusz Malinowski; Mario Fritz

Learnable Pooling Regions for Image Classification Technical Report

arXiv:1301.3516 [cs.CV], 2013, (Workshop at International Conference on Learning Representations).

Links | BibTeX | Tags: 2013

@techreport{malinowski13arxiv,
title = {Learnable Pooling Regions for Image Classification},
author = {Mateusz Malinowski and Mario Fritz},
url = {http://arxiv.org/abs/1301.3516
http://arxiv.org/pdf/1301.3516v1.pdf},
year = {2013},
date = {2013-01-01},
journal = {CoRR},
volume = {abs/1301.3516},
organization = {CoRR, abs/1301.3516},
type = {arXiv:1301.3516 [cs.CV]},
note = {Workshop at International Conference on Learning Representations},
keywords = {2013},
pubstate = {published},
tppubtype = {techreport}
}

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  • http://arxiv.org/abs/1301.3516
  • http://arxiv.org/pdf/1301.3516v1.pdf

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2012

Inproceedings

Kernel Density Topic Models: Visual Topics Without Visual Words

Konstantinos Rematas; Mario Fritz; Tinne Tuytelaars

Kernel Density Topic Models: Visual Topics Without Visual Words Inproceedings

In: NIPS Workshop Modern Non-Parametric Methods in Machine Learning, 2012.

Links | BibTeX | Tags: 2013

@inproceedings{rematas12NIPSW,
title = {Kernel Density Topic Models: Visual Topics Without Visual Words},
author = {Konstantinos Rematas and Mario Fritz and Tinne Tuytelaars},
url = {https://cispa.saarland/group/fritz/wp-content/blogs.dir/13/files/2020/06/rematas_kernel.pdf},
year = {2012},
date = {2012-12-01},
booktitle = {NIPS Workshop Modern Non-Parametric Methods in Machine Learning},
keywords = {2013},
pubstate = {published},
tppubtype = {inproceedings}
}

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  • https://cispa.saarland/group/fritz/wp-content/blogs.dir/13/files/2020/06/rematas[...]

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