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Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition.

, , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 5231-5240. PMLR, (2019)

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Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples., , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 274-283. PMLR, (2018)An Attack on InstaHide: Is Private Learning Possible with Instance Encoding?, , , , , , , , and . CoRR, (2020)Security of Machine Learning (Dagstuhl Seminar 22281)., , , , and . Dagstuhl Reports, 12 (7): 41-61 (July 2022)Session details: Session 1: Adversarial Machine Learning.. AISec@CCS, ACM, (2021)The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks., , , , and . USENIX Security Symposium, page 267-284. USENIX Association, (2019)Evading Deepfake-Image Detectors with White- and Black-Box Attacks., and . CVPR Workshops, page 2804-2813. Computer Vision Foundation / IEEE, (2020)Poisoning Web-Scale Training Datasets is Practical., , , , , , , , and . CoRR, (2023)Initialization Matters for Adversarial Transfer Learning., , , , , and . CoRR, (2023)Measuring Forgetting of Memorized Training Examples., , , , , , , , , and 1 other author(s). CoRR, (2022)Debugging Differential Privacy: A Case Study for Privacy Auditing., , , , , and . CoRR, (2022)