Abstract
The traditional image discriminating methods can accurately identify forged pictures generated by splicing, tampering, etc. But most methods cannot identify the forged pictures generated by the GAN models. In this paper, we specially explore to identify forged face created with the GAN models. Our target is to analyze the effect of data correlation on identification of computer created face images. In this work, we mainly test on false face datasets generated by StyleGAN and DCGAN. Both datasets are divided into two experimental control groups. We use the convolutional neural network models such as ResNet-18, VGG, and GoogLeNet to perform classification experiments on the control experimental groups. The results show that the models used in this paper can accurately distinguish the real faces and the forged faces generated with GAN. The validation analysis shows that the data correlation has a low influence on identification of forged faces with specific models.
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Tan, T., Wang, X., Fang, Y., Zhang, W. (2019). The Impact of Data Correlation on Identification of Computer-Generated Face Images. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_17
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DOI: https://doi.org/10.1007/978-3-030-31456-9_17
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