  Deep learning has been broadly leveraged by major cloud providers, such as
Google, AWS and Baidu, to offer various computer vision related services
including image classification, object identification, illegal image detection,
etc. While recent works extensively demonstrated that deep learning
classification models are vulnerable to adversarial examples, cloud-based image
detection models, which are more complicated than classifiers, may also have
similar security concern but not get enough attention yet. In this paper, we
mainly focus on the security issues of real-world cloud-based image detectors.
Specifically, (1) based on effective semantic segmentation, we propose four
attacks to generate semantics-aware adversarial examples via only interacting
with black-box APIs; and (2) we make the first attempt to conduct an extensive
empirical study of black-box attacks against real-world cloud-based image
detectors. Through the comprehensive evaluations on five major cloud platforms:
AWS, Azure, Google Cloud, Baidu Cloud, and Alibaba Cloud, we demonstrate that
our image processing based attacks can reach a success rate of approximately
100%, and the semantic segmentation based attacks have a success rate over 90%
among different detection services, such as violence, politician, and
pornography detection. We also proposed several possible defense strategies for
these security challenges in the real-life situation.
