Torsten Krauß
Chair of Software Engineering (Informatik II)
Department of Computer Science
University of Würzburg
Am Hubland, 97074, Würzburg, Germany
Informatikgebäude, 1.OG, Room A117
E-Mail: torsten.krauss@uni-wuerzburg.de
Phone: +49 931 31-81983
Occupation
Since Oct 2021 | PhD student in the Secure Software Systems Group at the Chair of Computer Science II - Software-Engineering, University of Würzburg |
Research interests
- AI for Security and Security for AI
- Security in Large Scale Machine Learning
- Trageted and Untargeted Poisoning Attacks on Machine Learning
- Federated Learning Security
- Dataset Cleaning
- Model Prediction Trust Scoring
- Covert Channels in Machine Learning Systems
- Machine Learning Model Watermarking for IP Protection
Projects
Teaching
WS 24/25
- Introduction to IT Security
SS 24
- Security of Software Systems
- Seminar IT Security
SS 23
- Security of Software Systems
- Seminar IT Security
WS 22/23
- Introduction to IT Security
SS 22
- Seminar IT Security
WS 21/22
- Introduction to IT Security
- Seminar IT Security
Publications
2024[ to top ]
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DNNShield: Embedding Identifiers for Deep Neural Network Ownership Verification. in ArXiv | arXiv:2403.06581 (2024).
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Cloud-Based Machine Learning Models as Covert Communication Channels. in the 19th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS 2024) (2024).
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CrowdGuard: Federated Backdoor Detection in Federated Learning. in the Network and Distributed System Security Symposium (NDSS) (2024).
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Automatic Adversarial Adaption for Stealthy Poisoning Attacks in Federated Learning. in the Network and Distributed System Security Symposium (NDSS) (2024).
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ClearStamp: A Human-Visible and Robust Model-Ownership Proof based on Transposed Model Training. in the 33rd USENIX Security Symposium (USENIX Security 2024) (2024).
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Verify your Labels! Trustworthy Predictions and Datasets via Confidence Scores. in the 33rd USENIX Security Symposium (USENIX Security 2024) (2024).
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2023[ to top ]
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ClearMark: Intuitive and Robust Model Watermarking via Transposed Model Training. in ArXiv | arXiv:2310.16453v1 (2023).
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MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers. in ACM Conference on Computer and Communications Security (CCS) (2023).
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Security of NVMe Offloaded Data in Large-Scale Machine Learning. in European Symposium on Research in Computer Security (ESORICS) (2023).
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Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations. in ArXiv | arXiv.2306.03600 (2023).
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2022[ to top ]
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Close the Gate: Detecting Backdoored Models in Federated Learning based on Client-Side Deep Layer Output Analysis. in ArXiv | arXiv:2210.07714 (2022).
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