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Towards Evaluating the Robustness of Neural Networks.

, and . IEEE Symposium on Security and Privacy, page 39-57. IEEE Computer Society, (2017)

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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)Security of Machine Learning (Dagstuhl Seminar 22281)., , , , and . Dagstuhl Reports, 12 (7): 41-61 (July 2022)An Attack on InstaHide: Is Private Learning Possible with Instance Encoding?, , , , , , , , and . CoRR, (2020)Students Parrot Their Teachers: Membership Inference on Model Distillation., , , , and . CoRR, (2023)Identifying and Mitigating the Security Risks of Generative AI., , , , , , , , , and 13 other author(s). CoRR, (2023)Poisoning Web-Scale Training Datasets is Practical., , , , , , , , and . CoRR, (2023)Initialization Matters for Adversarial Transfer Learning., , , , , and . CoRR, (2023)Publishing Efficient On-device Models Increases Adversarial Vulnerability., , and . CoRR, (2022)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)