A Certified Robustness Framework for Backdoor Poisoning Attacks using $f$-Divergences

Machine learning systems trained on untrusted data are vulnerable to data and backdoor poisoning attacks, in which an adversary corrupts training and test samples to manipulate predictions at inference time. Randomized smoothing yields provable defenses against such attacks, but existing application