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.

翻译：随着图像生成技术的快速发展，GAN生成指纹图像的恶意滥用在某些场景下对公共安全构成重大威胁。尽管现有通用的深度伪造检测方法可用于检测伪造指纹图像，但它们易受攻击且鲁棒性较差。同时，目前尚无专门针对指纹图像的深度伪造检测方法。本文首次提出针对指纹图像的深度伪造检测方法——据我们所知，该方法结合了指纹特有的脊线特征与GAN生成图像的生成伪影。具体而言，我们首先构建脊线流，利用沿脊线的灰度变化提取指纹特有特征；然后构建生成伪影流，利用输入指纹图像的FFT频谱提取更具鲁棒性的生成伪影特征；最后将脊线特有特征与生成伪影特征融合进行二分类（即真或假）。综合实验表明，所提方法在低复杂度下兼具有效性与鲁棒性。