  With the rapid development of the image generation technologies, the
malicious abuses of the GAN-generated fingerprint images poses a significant
threat to the public safety in certain circumstances. Although the existing
universal deep forgery detection approach can be applied to detect the fake
fingerprint images, they are easily attacked and have poor robustness.
Meanwhile, there is no specifically designed deep forgery detection method for
fingerprint images. In this paper, we propose the first deep forgery detection
approach for fingerprint images, which combines unique ridge features of
fingerprint and generation artifacts of the GAN-generated images, to the best
of our knowledge. Specifically, we firstly construct a ridge stream, which
exploits the grayscale variations along the ridges to extract unique
fingerprint-specific features. Then, we construct a generation artifact stream,
in which the FFT-based spectrums of the input fingerprint images are exploited,
to extract more robust generation artifact features. At last, the unique ridge
features and generation artifact features are fused for binary classification
(i.e., real or fake). Comprehensive experiments demonstrate that our proposed
approach is effective and robust with low complexities.
