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Machine learning has seen tremendous advances in the past few years, whichhas lead to deep learning models being deployed in varied applications ofday-to-day life. Attacks on such models using perturbations, particularly inreal-life scenarios, pose a severe challenge to their applicability, pushingresearch into the direction which aims to enhance the robustness of thesemodels. 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 thetargeted model and related parameters; and the black-box, which resembles areal-life scenario with the adversary having almost no knowledge of the modelto be attacked. To provide a comprehensive security cover, it is essential toidentify, study, and build defenses against such attacks. Hence, in this paper,we propose to present a comprehensive comparative study of various black-boxadversarial attacks and defense techniques.

A Survey of Black-Box Adversarial Attacks on Computer Vision Models