DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local
  Smoothing
Jiawei Zhang Zhongzhu Chen Huan Zhang Chaowei Xiao Bo Li
Diffusion models have been leveraged to perform adversarial purification and
thus provide both empirical and certified robustness for a standard model. On
the other hand, different robustly trained smoothed models have been studied to
improve the certified robustness. Thus, it raises a natural question: Can
diffusion model be used to achieve improved certified robustness on those
robustly trained smoothed models? In this work, we first theoretically show
that recovered instances by diffusion models are in the bounded neighborhood of
the original instance with high probability; and the "one-shot" denoising
diffusion probabilistic models (DDPM) can approximate the mean of the generated
distribution of a continuous-time diffusion model, which approximates the
original instance under mild conditions. Inspired by our analysis, we propose a
certifiably robust pipeline DiffSmooth, which first performs adversarial
purification via diffusion models and then maps the purified instances to a
common region via a simple yet effective local smoothing strategy. We conduct
extensive experiments on different datasets and show that DiffSmooth achieves
SOTA-certified robustness compared with eight baselines. For instance,
DiffSmooth improves the SOTA-certified accuracy from $36.0\%$ to $53.0\%$ under
$\ell_2$ radius $1.5$ on ImageNet. The code is available at
[https://github.com/javyduck/DiffSmooth].