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Towards more Practical Threat Models in Artificial Intelligence Security., , , and . CoRR, (2023)Machine Learning Security in Industry: A Quantitative Survey., , , , and . IEEE Trans. Inf. Forensics Secur., (2023)Rethinking data augmentation for adversarial robustness., , , , , , , and . Inf. Sci., (January 2024)Do winning tickets exist before DNN training?, and . SDM, page 549-557. SIAM, (2021)Killing Four Birds with one Gaussian Process: The Relation between different Test-Time Attacks., , and . ICPR, page 4696-4703. IEEE, (2020)MLCapsule: Guarded Offline Deployment of Machine Learning as a Service., , , , , , and . CVPR Workshops, page 3300-3309. Computer Vision Foundation / IEEE, (2021)Why is Machine Learning Security so hard?. Saarland University, Saarbrücken, Germany, (2020)An Argument-based Approach to Mining Opinions from Twitter., , and . AT, volume 918 of CEUR Workshop Proceedings, page 408-422. CEUR-WS.org, (2012)Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness., , , , , , , , , and 2 other author(s). CoRR, (2024)Adversarial Vulnerability Bounds for Gaussian Process Classification., , , and . CoRR, (2019)