Close the Gate: Detecting Backdoored Models in Federated Learning based on Client-Side Deep Layer Output Analysis. - Phillip Rieger; Torsten Krauß; Markus Miettinen; Alexandra Dmitrienko; Ahmad-Reza Sadeghi (CoRR, 2022)

MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers. - Torsten Krauß; Alexandra Dmitrienko (CCS, 2023)

Security of NVMe Offloaded Data in Large-Scale Machine Learning. - Torsten Krauß; Raphael Götz; Alexandra Dmitrienko (ESORICS (4), 2023)

Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations. - Torsten Krauß; Alexandra Dmitrienko (CoRR, 2023)

ClearMark: Intuitive and Robust Model Watermarking via Transposed Model Training. - Torsten Krauß; Jasper Stang; Alexandra Dmitrienko (CoRR, 2023)

Cloud-Based Machine Learning Models as Covert Communication Channels. - Torsten Krauß; Jasper Stang; Alexandra Dmitrienko (AsiaCCS, 2024)

Automatic Adversarial Adaption for Stealthy Poisoning Attacks in Federated Learning. - Torsten Krauß; Jan König; Alexandra Dmitrienko; Christian Kanzow (NDSS, 2024)

CrowdGuard: Federated Backdoor Detection in Federated Learning. - Phillip Rieger; Torsten Krauß; Markus Miettinen; Alexandra Dmitrienko; Ahmad-Reza Sadeghi (NDSS, 2024)

Verify your Labels! Trustworthy Predictions and Datasets via Confidence Scores. - Torsten Krauß; Jasper Stang; Alexandra Dmitrienko (USENIX Security Symposium, 2024)

ClearStamp: A Human-Visible and Robust Model-Ownership Proof based on Transposed Model Training. - Torsten Krauß; Jasper Stang; Alexandra Dmitrienko (USENIX Security Symposium, 2024)

DNNShield: Embedding Identifiers for Deep Neural Network Ownership Verification. - Jasper Stang; Torsten Krauß; Alexandra Dmitrienko (CoRR, 2024)

TwinBreak: Jailbreaking LLM Security Alignments based on Twin Prompts. - Torsten Krauß; Hamid Dashtbani; Alexandra Dmitrienko (CoRR, 2025)