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Towards generalizable detection of face forgery via self-guided model-agnostic learning., , , , , and . Pattern Recognit. Lett., (2022)Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples., , , and . CoRR, (2017)Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving., , , , , , , and . CoRR, (2023)Towards Transferable Targeted 3D Adversarial Attack in the Physical World., , , , , and . CoRR, (2023)Rethinking Model Ensemble in Transfer-based Adversarial Attacks., , , and . CoRR, (2023)Bag of Tricks for Adversarial Training., , , , and . CoRR, (2020)Black-box Detection of Backdoor Attacks with Limited Information and Data., , , , , , and . ICCV, page 16462-16471. IEEE, (2021)Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving., , , , , , , , and . CVPR, page 1022-1032. IEEE, (2023)Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart., , , , , , , and . CVPR, page 15202-15212. IEEE, (2022)AutoDA: Automated Decision-based Iterative Adversarial Attacks., , , , and . USENIX Security Symposium, page 3557-3574. USENIX Association, (2022)