Black-box attack methods aim to infer suitable attack patterns to targeted
DNN models by only using output feedback of the models and the corresponding
input queries. However, due to lack of prior and inefficiency in leveraging the
query and feedback information, existing methods are mostly query-intensive for
obtaining effective attack patterns. In this work, we propose a meta attack
approach that is capable of attacking a targeted model with much fewer queries.
Its high queryefficiency stems from effective utilization of meta learning
approaches in learning generalizable prior abstraction from the previously
observed attack patterns and exploiting such prior to help infer attack
patterns from only a few queries and outputs. Extensive experiments on MNIST,
CIFAR10 and tiny-Imagenet demonstrate that our meta-attack method can
remarkably reduce the number of model queries without sacrificing the attack
performance. Besides, the obtained meta attacker is not restricted to a
particular model but can be used easily with a fast adaptive ability to attack
a variety of models.The code of our work is available at
this https URL

本研究提出了一种元攻击方法，通过元学习的方法从以前观察到的攻击模式中学习可推广的先验抽象，并利用这样的先验信息，可以从很少的查询和输出中推断攻击模式，从而显着减少模型查询的数量，而不会牺牲攻击性能。