Unadversarial Examples: Designing Objects for Robust Vision

We study a class of computer vision settings wherein one can modify the design of the objects being recognized. We develop a framework that leverages this capability--and deep networks' unusual sensitivity to input perturbations--to design "robust objects," i.e., objects that are explicitly optimized to be confidently classified. Our framework yields improved performance on standard benchmarks, a simulated robotics environment, and physical-world experiments.