Summary: Among different adversarial attacks on deep learning models for image classification, physical attacks have been considered easier to implement without assuming access to victims’ devices.
Summary: Nowadays deep neural networks have been applied widely in many applications of computer vision including medical diagnosis and self-driving cars. However, deep neural networks are threatened by adversarial examples usually in which image pixels were perturbed unnoticeable to humans but enough to fool the deep networks.
Summary: Machine Learning as a Service on cloud not only provides a solution to scale demanding workloads, but also allows broader accessibility for the utilization of trained deep neural networks.
Summary: Implementation of a method of robust 3D adversarial attacks which considers different viewpoints where the victim camera can be placed. In particular, we find a method to create 3D adversarial examples that can achieve 100% attack success rate from all viewpoints with any integer spherical coordinates.