Improving the Convergence Rate of Ray Search Optimization for Query-Efficient Hard-Label Attacks

This article likely presents a novel method to enhance the efficiency of adversarial attacks against machine learning models. Specifically, it focuses on improving the speed at which these attacks converge, which is crucial for practical applications where query limits are imposed. The use of "Ray Search Optimization" suggests a specific algorithmic approach, and the context of "hard-label attacks" indicates the target models are treated as black boxes, only providing class labels as output. The research likely involves experimentation and evaluation to demonstrate the effectiveness of the proposed improvements.

• •Focuses on improving the efficiency of adversarial attacks.
• •Targets the convergence rate of attacks, important for query-limited scenarios.
• •Employs Ray Search Optimization, suggesting a specific algorithmic approach.
• •Deals with hard-label attacks, treating target models as black boxes.