Title: Stochastic Variance Reduced Ensemble Adversarial Attack. Abstract: Black-box adversarial attack has attracted much attention for its practical use in deep learning applications, and it is very challenging as there is no access to the architecture and weights of the target model. Based on the hypothesis that if an example remains adversarial for multiple models, then it is more likely to transfer to other models, the ensemble-based attack methods are efficient and widely used in the black-box setting.  Nevertheless, existing ensemble-based approaches simply aggregate the outputs of all models but ignore the variance of different models, leading to a rather poor local optimum. To address this issue, we propose a stochastic variance reduced ensemble attack method to boost the performance of black-box adversarial attacks. By integrating the stochastic variance reduced gradient technique into the model ensemble attack, our method can balance the gradient of different models and leads to a better local maximum, resulting in highly transferable adversarial examples. Empirical results on the standard ImageNet dataset demonstrate that our method can boost the ensemble attack performance and significantly improve the transferability of the generated adversarial examples. 