  Black-box adversarial attacks can fool image classifiers into misclassifying
images without requiring access to model structure and weights. Recent studies
have reported attack success rates of over 95% with less than 1,000 queries.
The question then arises of whether black-box attacks have become a real threat
against IoT devices that rely on cloud APIs to achieve image classification. To
shed some light on this, note that prior research has primarily focused on
increasing the success rate and reducing the number of queries. However,
another crucial factor for black-box attacks against cloud APIs is the time
required to perform the attack. This paper applies black-box attacks directly
to cloud APIs rather than to local models, thereby avoiding mistakes made in
prior research that applied the perturbation before image encoding and
pre-processing. Further, we exploit load balancing to enable distributed
black-box attacks that can reduce the attack time by a factor of about five for
both local search and gradient estimation methods.
