Decision-Based

This paper introduces D-BADGE, a novel approach for generating decision-based universal adversarial perturbations using random gradient-free optimization and batch attack techniques. By combining multiple adversarial examples into a single universal perturbation and reformulating the accuracy metric into a continuous Hamming distance form, D-BADGE achieves superior attack time efficiency compared to existing methods, successfully deceiving unseen victims and accurately targeting specific classes.