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Machine Learning in Adversarial Settings., , and . IEEE Security & Privacy, 14 (3): 68-72 (2016)Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples., , and . CoRR, (2016)Building Better Detection with Privileged Information., , , , and . CoRR, (2016)Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples., , , , , and . CoRR, (2016)Analyzing and Improving Representations with the Soft Nearest Neighbor Loss., , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 2012-2020. PMLR, (2019)Adversarial Examples that Fool both Computer Vision and Time-Limited Humans., , , , , , and . NeurIPS, page 3914-3924. (2018)Pipe Overflow: Smashing Voice Authentication for Fun and Profit., , , , , , and . CoRR, (2022)Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness., , , , and . CoRR, (2019)Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning., and . CoRR, (2018)Ensemble Adversarial Training: Attacks and Defenses., , , , and . CoRR, (2017)