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.

本研究探索了使用扩散模型对欺骗图像进行去噪和恢复真实图像的方法，通过比较这两个图像之间的差异，作为面部防伪的判别依据。实验结果表明，该方法在准确性和泛化性能方面具有竞争力。

通过去除欺骗扩散对欺骗噪声建模及其在人脸防欺骗中的应用