Deep learning models are prone to being fooled by imperceptible perturbations
known as adversarial attacks. In this work, we study how equipping models with
Test-time Transformation Ensembling (TTE) can work as a reliable defense
against such attacks. While transforming the input data, both at train and test
times, is known to enhance model performance, its effects on adversarial
robustness have not been studied. Here, we present a comprehensive empirical
study of the impact of TTE, in the form of widely-used image transforms, on
adversarial robustness. We show that TTE consistently improves model robustness
against a variety of powerful attacks without any need for re-training, and
that this improvement comes at virtually no trade-off with accuracy on clean
samples. Finally, we show that the benefits of TTE transfer even to the
certified robustness domain, in which TTE provides sizable and consistent

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