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- W3004996675 abstract "Machine learning has seen tremendous advances in the past few years, which has lead to deep learning models being deployed in varied applications of day-to-day life. Attacks on such models using perturbations, particularly in real-life scenarios, pose a severe challenge to their applicability, pushing research into the direction which aims to enhance the robustness of these models. After the introduction of these perturbations by Szegedy et al. [1], significant amount of research has focused on the reliability of such models, primarily in two aspects - white-box, where the adversary has access to the targeted model and related parameters; and the black-box, which resembles a real-life scenario with the adversary having almost no knowledge of the model to be attacked. To provide a comprehensive security cover, it is essential to identify, study, and build defenses against such attacks. Hence, in this paper, we propose to present a comprehensive comparative study of various black-box adversarial attacks and defense techniques." @default.
- W3004996675 created "2020-02-14" @default.
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- W3004996675 creator A5079120401 @default.
- W3004996675 date "2019-12-03" @default.
- W3004996675 modified "2023-09-27" @default.
- W3004996675 title "A Survey of Black-Box Adversarial Attacks on Computer Vision Models" @default.
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