Adversarial Robust Model Compression Using In-Train Pruning. - Manoj Rohit Vemparala; Nael Fasfous; Alexander Frickenstein; Sreetama Sarkar; Qi Zhao 011; Sabine Kuhn; Lukas Frickenstein; Anmol Singh; Christian Unger; Naveen Shankar Nagaraja; Christian Wressnegger; Walter Stechele (CVPR Workshops, 2021)

BreakingBED: Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks. - Manoj Rohit Vemparala; Alexander Frickenstein; Nael Fasfous; Lukas Frickenstein; Qi Zhao 011; Sabine Kuhn; Daniel Ehrhardt; Yuankai Wu; Christian Unger; Naveen Shankar Nagaraja; Walter Stechele (IntelliSys (1), 2021)

Non-Uniform Adversarially Robust Pruning. - Qi Zhao 011; Tim Köonigl; Christian Wressnegger (AutoML, 2022)

HW-Flow: A Multi-Abstraction Level HW-CNN Codesign Pruning Methodology. - Manoj Rohit Vemparala; Nael Fasfous; Alexander Frickenstein; Emanuele Valpreda; Manfredi Camalleri; Qi Zhao 011; Christian Unger; Naveen Shankar Nagaraja; Maurizio Martina; Walter Stechele (Leibniz Trans. Embed. Syst., 2022)

Holistic Adversarially Robust Pruning. - Qi Zhao 011; Christian Wressnegger (ICLR, 2023)

Adversarially Robust Anti-Backdoor Learning. - Qi Zhao 011; Christian Wressnegger (AISec@CCS, 2024)

Two Sides of the Same Coin: Learning the Backdoor to Remove the Backdoor. - Qi Zhao 011; Christian Wressnegger (AAAI, 2025)

S2AP: Score-space Sharpness Minimization for Adversarial Pruning. - Giorgio Piras; Qi Zhao 011; Fabio Brau; Maura Pintor; Christian Wressnegger; Battista Biggio (CoRR, 2025)