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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.

One paper accepted to IEEE Access.