Fritz, Mario

Adapting visual category models to new domains

Saenko, K., Kulis, B., Fritz, M., & Darrell, T. (2010) · Lecture Notes in Computer Science

Domain Adaptation and Few-Shot LearningAdvanced Image and Video Retrieval Techniques

Cited by 2,847

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

Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., & Backes, M. (2018) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 873

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

Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., & Backes, M. (2019) · Proceedings 2019 Network and Distributed System Security Symposium

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 852

The 2005 PASCAL Visual Object Classes Challenge

Everingham, M., Zisserman, A., Williams, C. K. I., Van Gool, L., Allan, M., Bishop, C. M., Chapelle, O., Dalal, N., Deselaers, T., Dorkó, G., et al. (2006) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 765

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

Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023) · Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 667

Knockoff Nets: Stealing Functionality of Black-Box Models

Orekondy, T., Schiele, B., & Fritz, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Adversarial Robustness in Machine LearningDomain Adaptation and Few-Shot Learning

Cited by 549

GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models

Chen, D., Yu, N., Zhang, Y., & Fritz, M. (2020) · Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 311

Towards Reverse-Engineering Black-Box Neural Networks

Oh, S. J., Schiele, B., & Fritz, M. (2019) · Lecture Notes in Computer Science

Adversarial Robustness in Machine LearningExplainable Artificial Intelligence (XAI)

Cited by 291

Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data

Yu, N., Skripniuk, V., Abdelnabi, S., & Fritz, M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Digital Media Forensic DetectionAdvanced Steganography and Watermarking Techniques

Cited by 195

Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning

Salem, A., Bhattacharya, A., Backes, M., Fritz, M., & Zhang, Y. (2020) · Universität des Saarlandes

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 157

Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis

He, Y., Yu, N., Keuper, M., & Fritz, M. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 70

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

Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 50

Hidden in Memory: Sleeper Memory Poisoning in LLM Agents

Pulipaka, S., Hlebik, S., Raghav, L., Abdelnabi, S., Raina, V., Sheth, I., & Fritz, M. (2026) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 37

Differential Privacy Defenses and Sampling Attacks for Membership Inference

Rahimian, S., Orekondy, T., & Fritz, M. (2021) · Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 24

LLM2Swarm: Robot Swarms that Responsively Reason, Plan, and Collaborate through LLMs

Strobel, V., Dorigo, M., & Fritz, M. (2024) · arXiv

Modular Robots and Swarm IntelligenceDistributed Control Multi-Agent Systems

Cited by 22

LLM Task Interference: An Initial Study on the Impact of Task-Switch in Conversational History

Gupta, A., Sheth, I., Raina, V., Gales, M., & Fritz, M. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Speech and dialogue systemsTopic Modeling

Cited by 10

Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation

He, Y., Rahimian, S., Schiele, B., & Fritz, M. (2019) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 10

Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews

Vasu, S. S. M., Sheth, I., Wang, H.-P., Binkyte, R., & Fritz, M. (2025) · arXiv

Ethics and Social Impacts of AIscientometrics and bibliometrics research

Cited by 6

Bayesian Prediction of Future Street Scenes through Importance Sampling based Optimization

Bhattacharyya, A., Fritz, M., & Schiele, B. (2018) · arXiv

Advanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety

Cited by 2

Can LLMs Separate Instructions From Data? And What Do We Even Mean By That?

Zverev, E., Abdelnabi, S., Tabesh, S., Fritz, M., & Lampert, C. H. (2024) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 1

Safety Must Precede the Deployment of Open-Ended AI

Sheth, I., Wehner, J., Abdelnabi, S., Binkyte, R., & Fritz, M. (2025) · arXiv

Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Cited by 0