  Diffusion Purification, purifying noised images with diffusion models, has
been widely used for enhancing certified robustness via randomized smoothing.
However, existing frameworks often grapple with the balance between efficiency
and effectiveness. While the Denoising Diffusion Probabilistic Model (DDPM)
offers an efficient single-step purification, it falls short in ensuring
purified images reside on the data manifold. Conversely, the Stochastic
Diffusion Model effectively places purified images on the data manifold but
demands solving cumbersome stochastic differential equations, while its
derivative, the Probability Flow Ordinary Differential Equation (PF-ODE),
though solving simpler ordinary differential equations, still requires multiple
computational steps. In this work, we demonstrated that an ideal purification
pipeline should generate the purified images on the data manifold that are as
much semantically aligned to the original images for effectiveness in one step
for efficiency. Therefore, we introduced Consistency Purification, an
efficiency-effectiveness Pareto superior purifier compared to the previous
work. Consistency Purification employs the consistency model, a one-step
generative model distilled from PF-ODE, thus can generate on-manifold purified
images with a single network evaluation. However, the consistency model is
designed not for purification thus it does not inherently ensure semantic
alignment between purified and original images. To resolve this issue, we
further refine it through Consistency Fine-tuning with LPIPS loss, which
enables more aligned semantic meaning while keeping the purified images on data
manifold. Our comprehensive experiments demonstrate that our Consistency
Purification framework achieves state-of the-art certified robustness and
efficiency compared to baseline methods.
