BTUAP: Boosting the Transferability of Universal Adversarial Perturbations in the Black-box Setting under various data dependencies

Deep neural networks (DNNs) are susceptible to Universal Adversarial Perturbation (UAP), which significantly increases the likelihood of deceiving DNNs. Current UAP generation methods are categorized into data-dependent, relaxed-data-free, and data-free attacks based on different data dependencies. However, both strategies exhibit poor transferability in the black-box settings. To address this limitation, we propose BTUAP, a novel UAP generation method designed to enhance the transferability of UAP in the black-box setting. BTUAP employs an ensemble strategy with min-max weight adjustment mechanisms to reduce the impact of model characteristics and introduces a self-supervised optimization strategy to maximize the distance of predicted logits between benign samples and adversarial examples. Experimental results demonstrate that BTUAP significantly improves transferability in different data dependency settings under black-box constraints. We also quantify the impact of the distribution shift and provide a new metric to measure the robustness of models. The source code is available at htps://github.com/RaymondDawn/BTUAP.