Query-efficient Meta Attack to Deep Neural Networks

By Jiawei Du, Hu Zhang, Joey Tianyi Zhou, Yi Yang, Jiashi Feng. 2019

88 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Safety, Robustness & Interpretability

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Citations in the corpus

Square Attack: a query-efficient black-box adversarial attack via random search (2019, background): “, when the attacker already has a substitute model similar to the target one [44,57,17,23,52] and thus can generate adversarial examples for the substitute model and then transfer them to the target model.”
Advances in adversarial attacks and defenses in computer vision: A survey (2021, methodology): “Similarly, to improve query efficiency, a technique to extract generalizable prior using the earlier queries with meta learning is proposed in [78].”