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A deep convolutional generative adversarial network-based fake fingerprint generation method

Fingerprints recognition (FPR) is considered as a secure and efficient way of personal identification verification. However, research of fake fingerprints generation by generative adversarial networks (GAN) disclosed a potential vulnerability of partial FPR systems. In this paper, we proposed two networks based on deep convolutional generative adversarial networks (DCGAN) to improve the performance of DCGAN by enhancing the performance of the discriminator. More specifically, the structure of discriminators in our network is modified by two approximate typical convolutional neural networks, AlexNet and VGG11. (We abbreviated them to Alex-GAN and VGG-GAN respectively) We evaluated the output of generators in our models by cosine similarity index every 10 epochs during training, which monitored the performance of the generator and was convenient for comparing two models. The results of cosine similarity and texture analysis show that VGG-GAN performs better than Alex-GAN. Although our models are unstable and unbalanced in discriminator and generator, we acquired notable results unexpectedly which demonstrate the competitiveness of our model.