基于模型间迁移性的黑盒对抗攻击起点提升方法

为高效地寻找基于决策的黑盒攻击下的对抗样本,提出一种利用模型之间的迁移性提升对抗起点的方法.通过模型之间的迁移性来循环叠加干扰图像,生成初始样本作为新的攻击起点进行边界攻击,实现基于决策的无目标黑盒对抗攻击和有目标黑盒对抗攻击.实验结果表明,无目标攻击节省了23％的查询次数,有目标攻击节省了17％的查询次数,且整个黑盒...

Black Box Adversarial Attack Starting Point Promotion Method Based on Mobility Between Models

In order to efficiently find the adversarial samples under the decision-based black box attacks, a method using the mobility between models is proposed to enhance the adversarial starting point. The mobility is used to circularly superimpose interference images, and samples are generated as a new starting point for boundary attacks. Thus the decision making-based non-target adversarial black box attacks and targeted adversarial black box attacks are realized. Experimental results show that the query times required for the non-target attacks is reduced by 23%, and that required for the targeted attacks is reduced by 17%. Moreover, the whole black box attack algorithm takes less time than the original boundary attack algorithm.