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Adversarial Images Against Super-Resolution Convolutional Neural Networks for Free.

, , , and . Proc. Priv. Enhancing Technol., 2022 (3): 120-139 (2022)

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Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection., , , , and . CoRR, (2018)LDL: A Defense for Label-Based Membership Inference Attacks., , , , and . AsiaCCS, page 95-108. ACM, (2023)Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning., , , and . CoRR, (2018)Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks., , , and . Canadian AI, volume 12109 of Lecture Notes in Computer Science, page 1-14. Springer, (2020)The Trojan Detection Challenge., , , , , , , , , and 9 other author(s). NeurIPS (Competition and Demos), volume 220 of Proceedings of Machine Learning Research, page 279-291. PMLR, (2021)MDTD: A Multi Domain Trojan Detector for Deep Neural Networks., , , , , , and . CoRR, (2023)Trojan Horse Training for Breaking Defenses against Backdoor Attacks in Deep Learning., , and . CoRR, (2022)Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs., and . CoRR, (2020)Resilience Against Data Manipulation in Distributed Synchrophasor-Based Mode Estimation., and . IEEE Trans. Smart Grid, 12 (4): 3538-3547 (2021)On the (Im)Practicality of Adversarial Perturbation for Image Privacy., , , , and . Proc. Priv. Enhancing Technol., 2021 (1): 85-106 (2021)