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Black-box Detection of Backdoor Attacks with Limited Information and Data., , , , , , and . ICCV, page 16462-16471. IEEE, (2021)Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples., , , and . CoRR, (2017)AutoDA: Automated Decision-based Iterative Adversarial Attacks., , , , and . USENIX Security Symposium, page 3557-3574. USENIX Association, (2022)Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving., , , , , , , , and . CVPR, page 1022-1032. IEEE, (2023)Understanding and Exploring the Network with Stochastic Architectures., , , and . NeurIPS, (2020)Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart., , , , , , , and . CVPR, page 15202-15212. IEEE, (2022)Kallima: A Clean-Label Framework for Textual Backdoor Attacks., , , , , and . ESORICS (1), volume 13554 of Lecture Notes in Computer Science, page 447-466. Springer, (2022)Composite Binary Decomposition Networks., , , , , and . AAAI, page 4747-4754. AAAI Press, (2019)Towards Privacy Protection by Generating Adversarial Identity Masks., , , , and . CoRR, (2020)Improving Transferability of Adversarial Patches on Face Recognition With Generative Models., , , , , , , and . CVPR, page 11845-11854. Computer Vision Foundation / IEEE, (2021)