  Deep neural networks(DNNs) is vulnerable to be attacked by adversarial
examples. Black-box attack is the most threatening attack. At present,
black-box attack methods mainly adopt gradient-based iterative attack methods,
which usually limit the relationship between the iteration step size, the
number of iterations, and the maximum perturbation. In this paper, we propose a
new gradient iteration framework, which redefines the relationship between the
above three. Under this framework, we easily improve the attack success rate of
DI-TI-MIM. In addition, we propose a gradient iterative attack method based on
input dropout, which can be well combined with our framework. We further
propose a multi dropout rate version of this method. Experimental results show
that our best method can achieve attack success rate of 96.2\% for defense
model on average, which is higher than the state-of-the-art gradient-based
attacks.
