Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart

Understand

Correctly classifying adversarial examples is an essential but challenging requirement for safely deploying machine learning models.

• As reported in RobustBench, even the state-of-the-art adversarially trained models struggle to exceed 67% robust test accuracy on CIFAR-10, which is far from practical.
• A complementary way towards robustness is to introduce a rejection option, allowing the model to not return predictions on uncertain inputs, where confidence is a commonly used certainty proxy.
• Along with this routine, we find that confidence and a rectified confidence (R-Con) can form two coupled rejection metrics, which could provably distinguish wrongly classified inputs from correctly classified ones.