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An Attack on InstaHide: Is Private Learning Possible with Instance Encoding?, , , , , , , , и . CoRR, (2020)Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples., , и . ICML, том 80 из Proceedings of Machine Learning Research, стр. 274-283. PMLR, (2018)Security of Machine Learning (Dagstuhl Seminar 22281)., , , , и . Dagstuhl Reports, 12 (7): 41-61 (июля 2022)Students Parrot Their Teachers: Membership Inference on Model Distillation., , , , и . CoRR, (2023)Identifying and Mitigating the Security Risks of Generative AI., , , , , , , , , и 13 other автор(ы). CoRR, (2023)Poisoning Web-Scale Training Datasets is Practical., , , , , , , , и . CoRR, (2023)Initialization Matters for Adversarial Transfer Learning., , , , , и . CoRR, (2023)Publishing Efficient On-device Models Increases Adversarial Vulnerability., , и . CoRR, (2022)Session details: Session 1: Adversarial Machine Learning.. AISec@CCS, ACM, (2021)Measuring Forgetting of Memorized Training Examples., , , , , , , , , и 1 other автор(ы). CoRR, (2022)