  Recently Diffusion-based Purification (DiffPure) has been recognized as an
effective defense method against adversarial examples. However, we find
DiffPure which directly employs the original pre-trained diffusion models for
adversarial purification, to be suboptimal. This is due to an inherent
trade-off between noise purification performance and data recovery quality.
Additionally, the reliability of existing evaluations for DiffPure is
questionable, as they rely on weak adaptive attacks. In this work, we propose a
novel Adversarial Diffusion Bridge Model, termed ADBM. ADBM directly constructs
a reverse bridge from the diffused adversarial data back to its original clean
examples, enhancing the purification capabilities of the original diffusion
models. Through theoretical analysis and experimental validation across various
scenarios, ADBM has proven to be a superior and robust defense mechanism,
offering significant promise for practical applications.
