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
(\textit{i.e.}, real or fake). Comprehensive experiments demonstrate that our
proposed approach is effective and robust with low complexities.

我们提出了首个用于指纹图像的深度伪造检测方法，结合了指纹的唯一脊线特征和 GAN 生成图像的生成痕迹特征，通过二分类（真实或伪造）将二者结合起来，实验结果表明这种方法具有低复杂度和较好的有效性。