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Practical Black-Box Attacks on Deep Neural Networks Using Efficient Query Mechanisms., , , и . ECCV (12), том 11216 из Lecture Notes in Computer Science, стр. 158-174. Springer, (2018)Proof of Luck: an Efficient Blockchain Consensus Protocol., , , и . SysTEX@Middleware, стр. 2:1-2:6. ACM, (2016)Adaptive and Diverse Techniques for Generating Adversarial Examples.. University of California, Berkeley, USA, (2018)base-search.net (ftcdlib:qt7jb8w89b).Spatially Transformed Adversarial Examples., , , , , и . ICLR (Poster), OpenReview.net, (2018)Generating Adversarial Examples with Adversarial Networks., , , , , и . IJCAI, стр. 3905-3911. ijcai.org, (2018)Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contracts., , , , , , , , и . EuroS&P, стр. 185-200. IEEE, (2019)Adversarial Example Defense: Ensembles of Weak Defenses are not Strong., , , , и . WOOT, USENIX Association, (2017)Unified Power Frequency Model Framework., , , , , и . ISLPED, стр. 174-179. ACM, (2016)Black-box Attacks on Deep Neural Networks via Gradient Estimation., , , и . ICLR (Workshop), OpenReview.net, (2018)Decision Boundary Analysis of Adversarial Examples., , и . ICLR (Poster), OpenReview.net, (2018)