  Face anti-spoofing is crucial for ensuring the security and reliability of
face recognition systems. Several existing face anti-spoofing methods utilize
GAN-like networks to detect presentation attacks by estimating the noise
pattern of a spoof image and recovering the corresponding genuine image. But
GAN's limited face appearance space results in the denoised faces cannot cover
the full data distribution of genuine faces, thereby undermining the
generalization performance of such methods. In this work, we present a
pioneering attempt to employ diffusion models to denoise a spoof image and
restore the genuine image. The difference between these two images is
considered as the spoof noise, which can serve as a discriminative cue for face
anti-spoofing. We evaluate our proposed method on several intra-testing and
inter-testing protocols, where the experimental results showcase the
effectiveness of our method in achieving competitive performance in terms of
both accuracy and generalization.
