  We consider the black-box adversarial setting, where the adversary has to
generate adversarial perturbations without access to the target models to
compute gradients. Previous methods tried to approximate the gradient either by
using a transfer gradient of a surrogate white-box model, or based on the query
feedback. However, these methods often suffer from low attack success rates or
poor query efficiency since it is non-trivial to estimate the gradient in a
high-dimensional space with limited information. To address these problems, we
propose a prior-guided random gradient-free (P-RGF) method to improve black-box
adversarial attacks, which takes the advantage of a transfer-based prior and
the query information simultaneously. The transfer-based prior given by the
gradient of a surrogate model is appropriately integrated into our algorithm by
an optimal coefficient derived by a theoretical analysis. Extensive experiments
demonstrate that our method requires much fewer queries to attack black-box
models with higher success rates compared with the alternative state-of-the-art
methods.
