Adversarial attacks have threatened the application of deep neural networks in security-sensitive scenarios. Most existing black-box attacks fool the target model by interacting with it many times and producing global perturbations. However, global perturbations change the smooth and insignificant background, which not only makes the perturbation more easily be perceived but also increases the query overhead. In this paper, we propose a novel framework to perturb the discriminative areas of clean examples only within limited queries in black-box attacks. Our framework is constructed based on two types of transferability. The first one is the transferability of model interpretations. Based on this property, we identify the discriminative areas of a given clean example easily for local perturbations. The second is the transferability of adversarial examples. It helps us to produce a local pre-perturbation for improving query efficiency. After identifying the discriminative areas and pre-perturbing, we generate the final adversarial examples from the pre-perturbed example by querying the targeted model with two kinds of black-box attack techniques, i.e., gradient estimation and random search. We conduct extensive experiments to show that our framework can significantly improve the query efficiency during black-box perturbing with a high attack success rate. Experimental results show that our attacks outperform state-of-the-art black-box attacks under various system settings.