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 family of fast gradient sign methods and improve their attack transferability 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. 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.

本文提出了一种名为查询先验方法的新方法，通过使用少量的查询来增强快速梯度符号法的攻击可传递性，理论分析和广泛的实验表明，我们的方法可以显著提高基于梯度的对抗攻击在CIFAR10/100和ImageNet上的可传递性，并优于具有相同少量查询的黑盒查询攻击。

通过少量查询提高对抗样本的可迁移性