When Should You Defend Your Classifier? - - A Game-Theoretical Analysis of Countermeasures Against Adversarial Examples. - Maximilian Samsinger; Florian Merkle; Pascal Schöttle; Tomás Pevný (GameSec, 2021)

Pruning in the Face of Adversaries. - Florian Merkle; Maximilian Samsinger; Pascal Schöttle (ICIAP (1), 2022)

Pruning for Power: Optimizing Energy Efficiency in IoT with Neural Network Pruning. - Thomas Widmann; Florian Merkle; Martin Nocker; Pascal Schöttle (EANN, 2023)

Generating Invariance-Based Adversarial Examples: Bringing Humans Back into the Loop. - Florian Merkle; Mihaela Roxana Sirbu; Martin Nocker; Pascal Schöttle (ICIAP Workshops (1), 2023)

On the Effect of Adversarial Training Against Invariance-based Adversarial Examples. - Roland Rauter; Martin Nocker; Florian Merkle; Pascal Schöttle (ICMLT, 2023)

Less is More: The Influence of Pruning on the Explainability of CNNs. - David Weber; Florian Merkle; Pascal Schöttle; Stephan Schlögl (CoRR, 2023)

Reinforcement learning in autonomous multi-vehicle systems: A structured review. - Florian Merkle; Gregor Blossey; Stefan Haeussler; Manuel Schneckenreither (ICSCA, 2024)

On the Economics of Adversarial Machine Learning. - Florian Merkle; Maximilian Samsinger; Pascal Schöttle; Tomás Pevný (IEEE Trans. Inf. Forensics Secur., 2024)

Less Is More: The Influence of Pruning on the Explainability of CNNs. - Florian Merkle; David Weber; Pascal Schöttle; Stephan Schlögl; Martin Nocker (IEEE Access, 2025)

Large-Scale (Semi-)Automated Security Assessment of Consumer IoT Devices - A Roadmap. - Pascal Schöttle; Matthias Janetschek; Florian Merkle; Martin Nocker; Christoph Egger 003 (CoRR, 2025)