PostDoc Positions

2023-01-31 Saarbrücken

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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

 

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:


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:

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