Co-supervised or Supervised:

Jonas Schäfer, Robustness of Detection Method Against Adversarial Attacks in LLM-Generated English Text

Niklas Pauli, Studie über experimentelle Deep Learning Ansätze für UWB RF-Fingerprinting

Linus Buddrus, Preemptively pruning CH strategies by additively removing contribution of unverified neurons

Manuel Vinzenz Welte, Preemptively Removing Clever-Hans Strategies of Neural Network Models by Pruning Activations in a learned relevant Subspace

Rabea Mrosek, The Hidden Subgroup Problem, FU Berlin 2024

Peiran Li, GranuGAN: Data Augmentation for Granular Hate Speech Detection via Generative Adversarial Networks

Nadine Knapp, undisclosed

François-Nima Thobae, Selbstvalidierung mithilfe biometrischer Daten eines Eyetrackers am Beispiel der Erkennung von Deepfakeangriffen, FU Berlin 2023

Brendan Gerrit Laschke, Cryptographic Properties of Anonymous Credential Systems, FU Berlin 2023

Wiktoria Krawczyk, Implementing a simple deep learning neural network in C++ for federate learning for RIOT, FU Berlin 2023

Vishal Kumar Singh, Anomaly Detection using Spatio-Temporal Dynamic Graph Neural Networks, FU Berlin 2023

Michael Peter Hoffmann, Multilingual Hate Speech Detection on Social Media: Applying Transfer Learning Methods to Classify German, Italian and Spanish Posts, FU Berlin 2023

Alexander Apostolos Rademann, OAuth 2.0 damals und heute: Analyse sicherheitsrelevanter Eigenschaften durch Threat Modeling, FU Berlin 2023

Magdalena Betram, Towards a lattice-based signature scheme for privacy-preserving protocols, FU Berlin 2023

Tobias Schalau, A Contrastive Self-Supervised Learning Model for Network Intrusion Detection, FU Berlin 2023

Leon Dirmeier, Lösung von Delegierungsproblemen des Unternehmenskontos mittels Proxy-Re-Encryption, FU Berlin 2023

Yarmina Anna Meszaros, Taxonomy of Privacy Attacks in Machine Learning, FU Berlin 2023

Jonny Lam, Herausforderung bei der Integration von Zero Trust in bestehende Systeme und Prozesse einer traditionellen IT Sicherheitsarchitektur, FU Berlin 2023

Marisa Frizzi Nest, Exploration of checkpoints in the context of membership inference attacks, FU Berlin 2023

Ina Fendel, Group-based Membership Inference Attack against Machine Learning Models, FU Berlin 2023

Yussuf Mohammed Kassem, Distributed Deep Neuroevolution for Offloading in Multi-Access Cloud-Edge Computing Networks, FU Berlin 2023

Valentin Pickel, A Comprehensive Description of the Quantum HHL Algorithm and its Application in the Cryptanalysis of the AES, FU Berlin 2023

Paulin Deupmann, Untersuchung des quantencomputerresistenten kryptographischen Verfahren CRYSTALS-Kyber, FU Berlin 2022

Manar Zaboub, Signal Strength Prediction and Visualization in 5G networks, Fraunhofer Fokus & FU Berlin 2022

Di Wang, Evaluating and Adapting Existing Neural Network Watermarking Approaches to Online Learning Scenarios, FU Berlin 2021

Jannis Ihrig, Attacking Differentially Private CNNs Trained with PATE, FU Berlin 2021

Tim von Känel, Practical Evaluation of Neural Network Watermarking Approaches, FU Berlin 2021

Oussama Bouanani, The Inuence of Training Parameters and Architectural Choices on the Vulnerability of Neural Networks to Membership Inference Attacks, FU Berlin 2021

Daniel Sosnovchyk, Evaluating Privacy of Synthetic Data Through Metrics, FU Berlin 2021

Felix Peterka, Design von Abhörkodes mit Deep Learning, FU Berlin 2021

Cezary Tomasz Pilaszewicz, Optimization of the size of a quantum register for Shor’s algorithm, FU Berlin 2021

Adrian Miloradovic, Precomputional Improvement of Supersingular Isogeny Diffie-Hellman, FU Berlin 2021

Jan Benninger, Abhörkodes für Semantische Sicherheit, FU Berlin 2021

Jasper Seidensticker, Gitterbasierte Kryptoanalyse: Die Coppersmith Methode, FU Berlin 2021

Maximilian Weigand, Realization Of An eIDAS Validation Service, FU Berlin 2020

Manuel Vinzenz Welte