Title: Enhancing the Transferability of Adversarial Examples via a Few Queries and Fuzzy Domain Eliminating. Abstract: Due to the vulnerability of deep neural networks, the black-box attack has drawn great attention from the community. Though transferable priors decrease the query number of the black-box query attacks in recent efforts, the average number of queries is still larger than 100, which is easily affected by the number of queries limit policy. In this work, we propose a novel method called query prior-based method to enhance the attack transferability of the family of fast gradient sign methods by using a few queries. Specifically, for the untargeted attack, we find that the successful attacked adversarial examples prefer to be classified as the wrong categories with higher probability by the victim model. Therefore, the weighted augmented cross-entropy loss is proposed to reduce the gradient angle between the surrogate model and the victim model for enhancing the transferability of the adversarial examples. In addition, the fuzzy domain eliminating technique is proposed to avoid the generated adversarial examples getting stuck in the local optimum. Specifically, we define the fuzzy domain of the input example $x$ in the $\epsilon$-ball of $x$. Then, temperature scaling and fuzzy scaling are utilized to eliminate the fuzzy domain for enhancing the transferability of the generated adversarial examples. Theoretical analysis and extensive experiments demonstrate that our method could significantly improve the transferability of gradient-based adversarial attacks on CIFAR10/100 and ImageNet and outperform the black-box query attack with the same few queries.