Explore a comprehensive analysis of HopSkipJumpAttack, a query-efficient decision-based adversarial attack on trained models. Delve into the algorithm's development, theoretical foundations, and practical applications in generating adversarial examples using only output labels. Learn about the novel gradient direction estimation technique utilizing binary information at the decision boundary, and understand how it optimizes for both untargeted and targeted attacks using l_2 and l_∞ similarity metrics. Examine the theoretical analysis behind the proposed algorithms and gradient direction estimate. Discover how HopSkipJumpAttack outperforms state-of-the-art decision-based adversarial attacks in terms of model query efficiency and its effectiveness against widely-used defense mechanisms. Gain insights into various aspects of adversarial attacks, including motivations, notations, types, objectives, and existing work in the field.