  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.
