  Many recent works demonstrated that Deep Learning models are vulnerable to
adversarial examples.Fortunately, generating adversarial examples usually
requires white-box access to the victim model, and the attacker can only access
the APIs opened by cloud platforms. Thus, keeping models in the cloud can
usually give a (false) sense of security.Unfortunately, cloud-based image
classification service is not robust to simple transformations such as Gaussian
Noise, Salt-and-Pepper Noise, Rotation and Monochromatization. In this
paper,(1) we propose one novel attack method called Image Fusion(IF) attack,
which achieve a high bypass rate,can be implemented only with OpenCV and is
difficult to defend; and (2) we make the first attempt to conduct an extensive
empirical study of Simple Transformation (ST) attacks against real-world
cloud-based classification services. Through evaluations on four popular cloud
platforms including Amazon, Google, Microsoft, Clarifai, we demonstrate that ST
attack has a success rate of approximately 100% except Amazon approximately
50%, IF attack have a success rate over 98% among different classification
services. (3) We discuss the possible defenses to address these security
challenges.Experiments show that our defense technology can effectively defend
known ST attacks.
