Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks

Modern machine learning systems are susceptible to adversarial examples; inputs which clearly preserve the characteristic semantics of a given class, but whose classification is (usually confidently) incorrect. Existing approaches to adversarial defense generally rely on modifying the input, e.g.