World's Best Scientists 2026 revealed!
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Computer Science
Germany
2025

D-Index & Metrics

Computer Science

D-Index
72
Citations
22041
World Ranking
1667
National Ranking
67

Research.com Recognitions

  • 2025 - Research.com Computer Science in Germany Leader Award
  • 2023 - Research.com Computer Science in Germany Leader Award
  • 2022 - Research.com Computer Science in Germany Leader Award

Overview

Mario Fritz is affiliated with the Helmholtz Center for Information Security in Germany. Their research primarily falls within the field of Computer Science, with a substantial focus on Artificial Intelligence, Computer Vision and Pattern Recognition, and related subfields.

The scientist's work covers a range of main topics including:

  • Adversarial Robustness in Machine Learning
  • Privacy-Preserving Technologies in Data
  • Generative Adversarial Networks and Image Synthesis
  • Advanced Neural Network Applications
  • Explainable Artificial Intelligence (XAI)
  • Digital Media Forensic Detection
  • Anomaly Detection Techniques and Applications

Recent significant publications by Mario Fritz include:

  • "Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data," 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "A4NT: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation," 2023, MPG.PuRe (Max Planck Society)
  • "Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks," 2023, arXiv (Cornell University)
  • "GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators," 2023, OPAL (Open@LaTrobe) (La Trobe University)

Mario Fritz frequently collaborates with several coauthors, including:

  • Bernt Schiele
  • Sahar Abdelnabi
  • Dingfan Chen
  • Raouf Kerkouche
  • Ning Yu

The scientist has contributed extensively to various academic venues, with a notable number of publications in:

  • arXiv (Cornell University)
  • Proceedings on Privacy Enhancing Technologies
  • Lecture Notes in Computer Science
  • MPG.PuRe (Max Planck Society)
  • OPAL (Open@LaTrobe) (La Trobe University)

In terms of book contributions, Mario Fritz has published with La Trobe University, including:

  • "Pattern Recognition: 40th German Conference, GCPR 2018, Stuttgart, Germany, October 9-12, 2018, Proceedings," published in 2023

Best Publications

  • Adapting visual category models to new domains

    Kate Saenko;Brian Kulis;Mario Fritz;Trevor Darrell

  • Appearance-based gaze estimation in the wild

    Xucong Zhang;Yusuke Sugano;Mario Fritz;Andreas Bulling

  • Ask Your Neurons: A Neural-Based Approach to Answering Questions about Images

    Mateusz Malinowski;Marcus Rohrbach;Mario Fritz

  • ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

    Ahmed Salem;Yang Zhang;Mathias Humbert;Pascal Berrang

  • A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input

    Mateusz Malinowski;Mario Fritz

  • Discovery of activity patterns using topic models

    Tâm Huynh;Mario Fritz;Bernt Schiele

  • Disentangled Person Image Generation

    Liqian Ma;Qianru Sun;Stamatios Georgoulis;Luc Van Gool

  • MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation

    Xucong Zhang;Yusuke Sugano;Mario Fritz;Andreas Bulling

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

    Allison Janoch;Sergey Karayev;Yangqing Jia;Jonathan T. Barron

  • The 2005 PASCAL visual object classes challenge

    Mark Everingham;Andrew Zisserman;Christopher K. I. Williams;Luc Van Gool

  • Towards Reverse-Engineering Black-Box Neural Networks

    Seong Joon Oh;Bernt Schiele;Mario Fritz

  • Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints

    Ning Yu;Larry Davis;Mario Fritz

  • Advanced Steel Microstructural Classification by Deep Learning Methods.

    Seyed Majid Azimi;Seyed Majid Azimi;Dominik Britz;Michael Engstler;Mario Fritz

  • It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation

    Xucong Zhang;Yusuke Sugano;Mario Fritz;Andreas Bulling

  • On the Significance of Real‐World Conditions for Material Classification

    Eric Hayman;Barbara Caputo;Mario Fritz;Jan Olof Eklundh

  • Knockoff Nets: Stealing Functionality of Black-Box Models

    Tribhuvanesh Orekondy;Bernt Schiele;Mario Fritz

  • A category-level 3-D object dataset: Putting the Kinect to work

    Allison Janoch;Sergey Karayev;Yangqing Jia;Jonathan T. Barron

  • Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection

    Unknown

  • VConv-DAE: Deep Volumetric Shape Learning Without Object Labels

    Abhishek Sharma;Oliver Grau;Mario Fritz

  • A geometric approach to robotic laundry folding

    Stephen Miller;Jur Van Den Berg;Mario Fritz;Trevor Darrell

  • Towards Reverse-Engineering Black-Box Neural Networks

    Seong Joon Oh;Max Augustin;Bernt Schiele;Mario Fritz

Frequent Co-Authors

Bernt Schiele
Bernt Schiele Max Planck Institute for Informatics
Trevor Darrell
Trevor Darrell University of California, Berkeley
Andreas Bulling
Andreas Bulling University of Stuttgart
Tobias Ritschel
Tobias Ritschel University College London
Luc Van Gool
Luc Van Gool Institute for Computer Science, Artificial Intelligence and Technology (INSAIT)
Rodrigo Benenson
Rodrigo Benenson Google (United States)
Kate Saenko
Kate Saenko Boston University
Ales Leonardis
Ales Leonardis University of Birmingham
Marcus Rohrbach
Marcus Rohrbach Facebook (United States)

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