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Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods.

, and . AISec@CCS, page 3-14. ACM, (2017)

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Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples., , , , , , , and . CoRR, (2021)AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation., , , , and . CoRR, (2021)Report of the 1st Workshop on Generative AI and Law., , , , , , , , , and 25 other author(s). CoRR, (2023)Extracting Training Data from Large Language Models., , , , , , , , , and 2 other author(s). CoRR, (2020)Handcrafted Backdoors in Deep Neural Networks., , and . CoRR, (2021)Session details: Session 2A: Machine Learning for Cybersecurity.. AISec@CCS, ACM, (2021)Tight Auditing of Differentially Private Machine Learning., , , , , , , and . USENIX Security Symposium, page 1631-1648. USENIX Association, (2023)Poisoning the Unlabeled Dataset of Semi-Supervised Learning.. USENIX Security Symposium, page 1577-1592. USENIX Association, (2021)Extracting Training Data from Large Language Models., , , , , , , , , and 2 other author(s). USENIX Security Symposium, page 2633-2650. USENIX Association, (2021)Membership Inference Attacks From First Principles., , , , , and . SP, page 1897-1914. IEEE, (2022)