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Detecting adversarial examples with inductive Venn-ABERS predictors., , and . ESANN, (2019)Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems., , and . FUZZ-IEEE, page 1-8. IEEE, (2022)Lower bounds on the robustness to adversarial perturbations., , , and . NIPS, page 804-813. (2017)Detecting adversarial manipulation using inductive Venn-ABERS predictors., , and . Neurocomputing, (2020)An Introduction to Adversarially Robust Deep Learning., , and . IEEE Trans. Pattern Anal. Mach. Intell., 46 (4): 2071-2090 (April 2024)Regional Image Perturbation Reduces Lp Norms of Adversarial Examples While Maintaining Model-to-model Transferability., , , , , and . CoRR, (2020)Distillation of Deep Reinforcement Learning Models Using Fuzzy Inference Systems., , and . BNAIC/BENELEARN, volume 2491 of CEUR Workshop Proceedings, CEUR-WS.org, (2019)Inline Detection of DGA Domains Using Side Information., , , , and . IEEE Access, (2020)CharBot: A Simple and Effective Method for Evading DGA Classifiers., , , , , , , and . IEEE Access, (2019)Hardening DGA Classifiers Utilizing IVAP., , , , and . IEEE BigData, page 6063-6065. IEEE, (2019)