Prof. Dr. Mario Fritz
Faculty
CISPA Helmholtz Center for Information Security
Professor
Saarland University
Fellow
European Laboratory for Learning and Intelligent Systems (ELLIS)
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)
2013
Incollections
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.
Inproceedings
Sequential Bayesian Model Update under Structured Scene Prior for Semantic Road Scenes Labeling Inproceedings
In: IEEE International Conference on Computer Vision (ICCV), 2013.
Learning Smooth Pooling Regions for Visual Recognition Inproceedings
In: British Machine Vision Conference (BMVC), 2013.
Dynamic Feature Selection for Classification on a Budget Inproceedings
In: ICML Workshop on Prediction with Sequential Models, 2013.
Masters Theses
Scene Segmentation in Adverse Vision Conditions Masters Thesis
Saarland University, 2013.
Multi-Scale Feature Learning for Material Recognition Masters Thesis
Saarland University, 2013.
Technical Reports
Learnable Pooling Regions for Image Classification Technical Report
arXiv:1301.3516 [cs.CV], 2013, (Workshop at International Conference on Learning Representations).
2012
Inproceedings
Kernel Density Topic Models: Visual Topics Without Visual Words Inproceedings
In: NIPS Workshop Modern Non-Parametric Methods in Machine Learning, 2012.