  Adaptive test-time defenses are used to improve the robustness of deep neural
networks to adversarial examples. However, existing methods significantly
increase the inference time due to additional optimization on the model
parameters or the input at test time. In this work, we propose a novel adaptive
test-time defense strategy that is easy to integrate with any existing (robust)
training procedure without additional test-time computation. Based on the
notion of robustness of features that we present, the key idea is to project
the trained models to the most robust feature space, thereby reducing the
vulnerability to adversarial attacks in non-robust directions. We theoretically
show that the top eigenspace of the feature matrix are more robust for a
generalized additive model and support our argument for a large width neural
network with the Neural Tangent Kernel (NTK) equivalence. We conduct extensive
experiments on CIFAR-10 and CIFAR-100 datasets for several robustness
benchmarks, including the state-of-the-art methods in RobustBench, and observe
that the proposed method outperforms existing adaptive test-time defenses at
much lower computation costs.
