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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References
Cao, K., Yang, X., Chen, X., et al.: A novel ant colony optimization algorithm for large-distorted fingerprint matching. Pattern Recogn. 45(1), 151–161 (2012)
Shin, A., Lee, S.W., Bulthoff, H., et al.: A morphable 3D-model of Korean faces. In: IEEE International Conference on Systems. IEEE (2012)
O’Dwyer, T.W., Nevitt, G.A.: Individual odor recognition in procellariiform chicks: potential role for the major histocompatibility complex. Ann. N. Y. Acad. Sci. 1170(1), 442–446 (2010)
Dang-Nguyen, D.T., Boato, G., Natale, F.D.G.B.: Identify computer generated characters by analysing facial expressions variation. In: WIFS. IEEE (2012)
Peng, B., Wang, W., Dong, J., et al.: Detection of computer generated faces in videos based on pulse signal. In: IEEE China Summit & International Conference on Signal and Information Processing. IEEE (2015)
Bayar, B., Stamm, M.C.: A deep learning approach to universal image manipulation detection using a new convolutional layer. In: The 4th ACM Workshop. ACM (2016)
Cai, S., Zhang, L., Zuo, W., et al.: A probabilistic collaborative representation based approach for pattern classification. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society (2016)
Rao, Y., Ni, J.: A deep learning approach to detection of splicing and copy-move forgeries in images. In: 2016 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE (2016)
Zhou, P., Han, X., Morariu, V.I., et al.: Two-stream neural networks for tampered face detection (2018)
Raghavendra, R., Raja, K.B., Venkatesh, S., et al.: Transferable deep-CNN features for detecting digital and print-scanned morphed face images. In: CVPRW. IEEE Computer Society (2017)
Rahmouni, N., Nozick, V., Yamagishi, J., et al.: Distinguishing computer graphics from natural images using convolution neural networks. In: Information Forensics and Security. IEEE (2018)
Tariq, S., Lee, S., Kim, H., et al.: Detecting both machine and human created fake face images in the wild. In: Proceedings of the 2nd International Workshop on Multimedia Privacy and Security - MPS 2018, Toronto, Canada, 15–19 October 2018, pp. 81–87 (2018)
Alex Leykin, F.C.: Differences of edge properties in photographs and paintings. In: International Conference on Image Processing. IEEE (2003)
Huang, Y.: Demosaicing recognition with applications in digital photo authentication based on a quadratic pixel correlation model. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2008)
Wang, X., Zhang, X., Li, Z., et al.: A DWT-DCT based passive forensics method for copy-move attacks. In: Third International Conference on Multimedia Information Networking and Security. IEEE (2011)
Wang, X., Xuan, B., Peng, S.L.: Digital image forgery detection based on the consistency of defocus blur. In: International Conference on Intelligent Information Hiding and Multimedia Signal Processing. IEEE Computer Society (2008)
He, K., Zhang, X., Ren, S., et al.: deep residual learning for image recognition (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science (2014)
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2015)
Yu, Y., Gong, Z., Zhong, P., Shan, J.: Unsupervised representation learning with deep convolutional neural network for remote sensing images. In: Zhao, Y., Kong, X., Taubman, D. (eds.) ICIG 2017. LNCS, vol. 10667, pp. 97–108. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71589-6_9
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4401–4410 (2019)
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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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