Decision-based methods have shown to be effective in black-box adversarial
attacks, as they can obtain satisfactory performance and only require to access
the final model prediction. Gradient estimation is a critical step in black-box
adversarial attacks, as it will directly affect the query efficiency. Recent
works have attempted to utilize gradient priors to facilitate score-based
methods to obtain better results. However, these gradient priors still suffer
from the edge gradient discrepancy issue and the successive iteration gradient
direction issue, thus are difficult to simply extend to decision-based methods.
In this paper, we propose a novel Decision-based Black-box Attack framework
with Gradient Priors (DBA-GP), which seamlessly integrates the data-dependent
gradient prior and time-dependent prior into the gradient estimation procedure.
First, by leveraging the joint bilateral filter to deal with each random
perturbation, DBA-GP can guarantee that the generated perturbations in edge
locations are hardly smoothed, i.e., alleviating the edge gradient discrepancy,
thus remaining the characteristics of the original image as much as possible.
Second, by utilizing a new gradient updating strategy to automatically adjust
the successive iteration gradient direction, DBA-GP can accelerate the
convergence speed, thus improving the query efficiency. Extensive experiments
have demonstrated that the proposed method outperforms other strong baselines
significantly.

提出了一种新颖的基于决策的黑盒攻击框架，DBA-GP，通过集成数据相关的梯度先验和时间相关的先验到梯度估计过程中，解决了边缘梯度差异和迭代梯度方向问题，从而提高了查询效率。广泛实验表明，该方法明显优于其他强基准方法。