  Deep neural networks (DNNs) have demonstrated excellent performance on
various tasks, however they are under the risk of adversarial examples that can
be easily generated when the target model is accessible to an attacker
(white-box setting). As plenty of machine learning models have been deployed
via online services that only provide query outputs from inaccessible models
(e.g. Google Cloud Vision API2), black-box adversarial attacks (inaccessible
target model) are of critical security concerns in practice rather than
white-box ones. However, existing query-based black-box adversarial attacks
often require excessive model queries to maintain a high attack success rate.
Therefore, in order to improve query efficiency, we explore the distribution of
adversarial examples around benign inputs with the help of image structure
information characterized by a Neural Process, and propose a Neural Process
based black-box adversarial attack (NP-Attack) in this paper. Extensive
experiments show that NP-Attack could greatly decrease the query counts under
the black-box setting.

Machine Learning;Machine Learning