Adaptive Randomized Smoothing: Certifying Multi-Step Defences against Adversarial Examples

We propose Adaptive Randomized Smoothing (ARS) to certify the predictions of our test-time adaptive models against adversarial examples. ARS extends the analysis of randomized smoothing using f-Differential Privacy to certify the adaptive composition of multiple steps. For the first time, our theory covers the sound adaptive composition of general and high-dimensional functions of noisy input. We instantiate ARS on deep image classification to certify predictions against adversarial examples of bounded L∞ norm. In the L∞ threat model, our flexibility enables adaptation through high-dimensional input-dependent masking. We design adaptivity benchmarks, based on CIFAR-10 and CelebA, and show that ARS improves accuracy by $2$ to $5%$ points. On ImageNet, ARS improves accuracy by $1$ to $3%$ points over standard RS without adaptivity.