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: PriSyn: Representative, synthetic health data with strong privacy guarantees
- Coordinator and PI: ImageTox: Automated image-based detection of early toxicity events in zebrafish larvae
- 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/misinformation detection, attribution, and responsible disclosure:
- AISec’23: Not what you’ve signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
- Usenix’23: UnGANable: Defending Against GAN-based Face Manipulation
- Usenix’23: 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:
- BlackHat’23 Compromising LLMs: The Advent of AI Malware
- CCS-W AISec’23: Certifiers Make Neural Networks Vulnerable to Availabi
- CCS-W AISec’23: Not what you’ve signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
- CCS-W WPES’23: Client-specific Property Inference against Secure Aggregation in Federated Learning
- GCPR’23: MargCTGAN: A “Marginally” Better CTGAN for the Low Sample Regime
- GCPR’23: Certified Robust Models with Slack Control and Large Lipschitz Constants
- Usenix’23: Fact-Saboteurs: A Taxonomy of Evidence Manipulation Attacks against Fact-Verification Systems
- 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
Most recent work on ArXiv:
- More than you’ve asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models
- Data Forensics in Diffusion Models: A Systematic Analysis of Membership Privacy
- Systematically Finding Security Vulnerabilities in Black-Box Code Generation Models
- Fed-GLOSS-DP: Federated, Global Learning using Synthetic Sets with Record Level Differential Privacy
- Holistically Explainable Vision Transformers
- SimSCOOD: Systematic Analysis of Out-of-Distribution Behavior of Source Code Models
- Availability Attacks Against Neural Network Certifiers Based on Backdoors
News, talks, events:
- Invited talk at ICCV’23 workshop BRAVO: roBustness and Reliability of Autonomous Vehicles in the Open-world
- Invited talk at ICCV’23 Workshop on DeepFake Analysis and Detection
- Invited talk at ICCV’23 Workshop on Out Of Distribution Generalization in Computer Vision
- Lecturer at ELLIS Summer School on Large-Scale AI for Research and Industry
- 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)
2020
Proceedings Articles
Prediction Poisoning: Utility-Constrained Defenses Against Model Stealing Attacks Proceedings Article
In: International Conference on Representation Learning (ICLR), 2020.
Technical Reports
InfoScrub: Towards Attribute Privacy by Targeted Obfuscation Technical Report
arXiv:2005.10329 , 2020.
Workshops
SampleFix: Learning to Correct Programs by Sampling Diverse Fixes Workshop
NeurIPS Workshop on Computer-Assisted Programming, 2020.
2019
Journal Articles
MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation Journal Article
In: Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019.
Book Sections
Towards reverse-engineering black-box neural networks Book Section
In: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 2019.
Proceedings Articles
Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints Proceedings Article
In: International Conference on Computer Vision (ICCV), 2019.
Deep Appearance Maps Proceedings Article
In: International Conference on Computer Vision (ICCV), 2019.
Knockoff Nets: Stealing Functionality of Black-Box Models Proceedings Article
In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
Not Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation Proceedings Article
In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
Time-Conditioned Action Anticipation in One Shot Proceedings Article
In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods Proceedings Article
In: International Conference on Representation Learning (ICLR), 2019.
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models Proceedings Article
In: Annual Network and Distributed System Security Symposium (NDSS), 2019.
Fashion is Taking Shape: Understanding Clothing Preference Based on Body Shape From Online Sources Proceedings Article
In: IEEE Winter Conference on Applications of Computer Vision (WACV), 2019.
Technical Reports
Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation Technical Report
arXiv:1912.09685, 2019.
"Best-of-Many-Samples" Distribution Matching Technical Report
arXiv:1909.12598, 2019.
GAN-Leaks: A Taxonomy of Membership Inference Attacks against GANs Technical Report
arXiv:1909.03935, 2019.
WhiteNet: Phishing Website Detection by Visual Whitelists Technical Report
arXiv:1909.00300, 2019.
Conditional Flow Variational Autoencoders for Structured Sequence Prediction Technical Report
arXiv:1908.09008, 2019.
Interpretability Beyond Classification Output: Semantic Bottleneck Networks Technical Report
arXiv:1907.10882 , 2019.
Prediction Poisoning: Utility-Constrained Defenses Against Model Stealing Attacks Technical Report
2019.
SampleFix: Learning to Correct Programs by Sampling Diverse Fixes Technical Report
arXiv:1906.10502, 2019.
Shape Evasion: Preventing Body Shape Inference of Multi-Stage Approaches Technical Report
arXiv:1905.11503 , 2019.
Learning Manipulation under Physics Constraints with Visual Perception Technical Report
2019.
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning Technical Report
2019.
Workshops
Differential Privacy Defenses and Sampling Attacks for Membership Inference Workshop
NeurIPS Workshop on Privacy in Machine Learning (PRIML), 2019.
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning Workshop
Hot Topics in Privacy Enhancing Technologies (HotPETs), 2019.
Understanding and Recognizing Bystanders in Images for Privacy Protection Workshop
Privacy, Usability, and Transparency (PUT) @ PETs, 2019.