Lower bounds on the robustness to adversarial perturbations. - Jonathan Peck; Joris Roels; Bart Goossens; Yvan Saeys (NIPS, 2017)

Hardening DGA Classifiers Utilizing IVAP. - Charles Grumer; Jonathan Peck; Femi G. Olumofin; Anderson C. A. Nascimento; Martine De Cock (IEEE BigData, 2019)

Distillation of Deep Reinforcement Learning Models Using Fuzzy Inference Systems. - Arne Gevaert; Jonathan Peck; Yvan Saeys (BNAIC/BENELEARN, 2019)

Calibrated Multi-probabilistic Prediction as a Defense Against Adversarial Attacks. - Jonathan Peck; Bart Goossens; Yvan Saeys (BNAIC/BENELEARN (Selected Papers), 2019)

Detecting adversarial examples with inductive Venn-ABERS predictors. - Jonathan Peck; Bart Goossens; Yvan Saeys (ESANN, 2019)

CharBot: A Simple and Effective Method for Evading DGA Classifiers. - Jonathan Peck; Claire Nie; Raaghavi Sivaguru; Charles Grumer; Femi G. Olumofin; Bin Yu; Anderson C. A. Nascimento; Martine De Cock (IEEE Access, 2019)

Inline Detection of DGA Domains Using Side Information. - Raaghavi Sivaguru; Jonathan Peck; Femi G. Olumofin; Anderson C. A. Nascimento; Martine De Cock (IEEE Access, 2020)

Regional Image Perturbation Reduces Lp Norms of Adversarial Examples While Maintaining Model-to-model Transferability. - Utku Ozbulak; Jonathan Peck; Wesley De Neve; Bart Goossens; Yvan Saeys; Arnout Van Messem (CoRR, 2020)

Detecting adversarial manipulation using inductive Venn-ABERS predictors. - Jonathan Peck; Bart Goossens; Yvan Saeys (Neurocomputing, 2020)

Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems. - Arne Gevaert; Jonathan Peck; Yvan Saeys (FUZZ-IEEE, 2022)

Robust width: A lightweight and certifiable adversarial defense. - Jonathan Peck; Bart Goossens (CoRR, 2024)

An Introduction to Adversarially Robust Deep Learning. - Jonathan Peck; Bart Goossens; Yvan Saeys (IEEE Trans. Pattern Anal. Mach. Intell., 2024)