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.

Authors

• Hadi Salman
• Andrew Ilyas
• Logan Engstrom
• Sai Vemprala
• Aleksander Madry
• Ashish Kapoor Microsoft Research

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