Abstract
With the rapid development of artificial intelligence technologies, various generative models can synthesize fake face images with photo-realistic effects. Glow, a generative flow using invertible 1×1 convolution, is a state-of-the-art technique for efficient synthesis of face images with high resolution and fidelity. However, facial forgeries bring serious challenges to morality, ethics and public confidence. Especially, facial forgeries might change the semantic content conveyed by a face image. A Convolutional Neural Network (CNN) based model, namely SCnet, is proposed to expose the Glow-based facial forgery. Specifically, an image sharpening operator is embedded in the convolutional layer as the pre-processing layer of the network to highlight the traces left by Glow. Then, SCnet is specifically designed to automatically learn high-level forensics features from pre-processing results. Moreover, a fake face dataset is built by exploiting the CelebA face image dataset and the Glow-based forgery technique. A series of experiments are conducted to prove the effectiveness of the proposed approach. Experimental results show that the proposed approach achieves a classification accuracy up to 95.92% under various post-processing operations.
Similar content being viewed by others
References
Afchar D, Nozick V, Yamagishi J, Echizen I (2018) Mesonet: a compact facial video forgery detection network. Proc. IEEE Int. Workshop Inf. Forensics Security, pp 1–7
Bastanfard A, Bastanfard O, Takahashi H, Nakajima M (2004) Toward anthropometrics simulation of face rejuvenation and skin cosmetic. Comput Animation Virt Worlds 15(3-4):347–352
Bayar B, Stamm MC (2018) Constrained convolutional neural networks: a new approach towards general purpose image manipulation detection. IEEE Trans Inf Forensic Secur 13(11):2691–2706
Bengio Y (2012) Practical recommendations for gradient-based training of deep architectures. Neural Networks: Tricks of the Trade. Springer, pp 437–478
Berthelot D., Schumm T., Metz L. Began: Boundary equilibrium generative adversarial networks. [Online]. Available: https://arxiv.org/abs/1703.10717
Choi Y, Choi M, Kim M, Ha J, Kim S, Choo J (2018) Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. Proceedings of CVPR, pp 8789–8797
Dang LM, Hassan SI, Im S, Lee J, Lee S, Moon H (2018) Deep learning based computer generated face identification using convolutional neural network. Appl Sci 8(12):2610–2628
Dinh L, Krueger D, Bengio Y Nice: Non-linear independent components estimation. [Online]. Available: https://arxiv.org/abs/1410.8516
Ding H, Sricharan K, Chellappa R (2018) Exprgan: Facial expression editing with controllable expression intensity. Proceedings of AAAI, pp 6781–6788
Dinh L, Sohldickstein J, Bengio S (2017) Density estimation using Real NVP. Proceedings of ICLR, pp 1–32
Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159
Experts: spy used AI-generated face to connect with targets via phantom LinkedIn profile. [Online]. Available: https://blackchristiannews.com/2019/06/experts-spy-used-ai-generated-face-to-connect-with-targets-via-phantom-linkedin-profile/
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Proceedings of NIPS, pp 2672–2680
Guo Z, Yang G, Chen J, Sun X (2020) Fake faces detection via adaptive residual prediction network. [Online]. Available: https://arxiv.org/abs/2005.04945
Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: Convolutional architecture for fast feature embedding. Proc. 22nd ACM Int. Conf. Multimedia, pp 675–678
Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T Analyzing and improving the image quality of StyleGAN. [Online]. Available: https://arxiv.org/abs/1912.04958
Karras T, Aila T, Laine S, Lehtinen J (2018) Progressive growing of GANs for improved quality, stability, and variation. Proceedings of ICLR, pp 1–26
Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. Proceedings of ICCV, pp 4401–4410
Kingma DP, Dhariwal P (2018) Glow: Generative flow with invertible 1×1 convolutions. Proceedings of NIPS, pp 10215–10224
Kingma DP, Salimans T, Jozefowicz R, Chen X et al, Sutskever I, Welling M (2016) Improved variational inference with inverse autoregressive flow. Proceedings of NIPS, pp 4743–4751
Korshunova I, Shi W, Dambre J, Theis L (2017) Fast face-swap using convolutional neural networks. Proceedings of ICCV, pp 3677–3685
LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551
LeCun Y, Bottou L, Orr GB, Müller K-R (2012) Efficient backprop. Neural Networks: Tricks of the Trade. Springer, pp 9–48
Li H, Luo W, Qiu X, Huang J (2018) Identification of various image operations using residual-based features. IEEE Trans Circ Syst Video Technol 28(1):31–45
Li L, Bao J, Zhang T, Yang H, Chen D, Wen F, Guo B (2020) Face x-ray for more general face forgery detection. Proceedings of CVPR
Lin M, Chen Q, Yan S (2014) Network in network. Proceedings of ICLR, pp 1–10
Liu Z, Luo P, Wang X, Tang X (2015) Deep learning face attributes in the wild. Proceedings of ICCV, pp 3730–3738
Mo H, Chen B, Luo W (2018) Fake faces identification via convolutional neural network. Proc. 6th ACM Workshop on Inf. Hid. Multimedia Security, pp 43–47
Nhu TD, Na IS, Kim SH (2018) Forensics face detection from GANs using convolutional neural network. Proc. Int. Symp. Inf. Technol. Convergence, pp 376–379
Parkhi OM, Vedaldi A, Zisserman A (2015) Deep face recognition. Proceedings of BMVC, pp 1–12
Pumarola A, Agudo A, Martinez AM, Sanfeliu A, Moreno-Noguer F (2018) Ganimation: anatomically-aware facial animation from a single image. Proceedings of ECCV, pp 818s–833
Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. Proceedings of ICLR, pp 1–16
Rössler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nießner M FaceForensics++: learning to detect manipulated facial images. [Online]. Available: https://arxiv.org/abs/1901.08971
Rössler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nießner M FaceForensics: a large-scale video dataset for forgery detection in human faces. [Online]. Available: https://arxiv.org/abs/1803.09179
Simonyan K, Zisserman A (2015) Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of ICLR, pp 1–14
Thies J, Zollhöfer M, Nießner M (2019) Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics
Thies J, Zollhöfer M, Stamminger M, Theobalt C, Nießner M (2016) Face2face: Real-time face capture and reenactment of RGB videos. Proceedings of CVPR, pp 2387–2395
Yu N, Davis L, Fritz M (2019) Attributing fake images to GANs: learning and analyzing GAN fingerprints. In: Proceedings of ICCV
Zhou P, Han X, Morariu VI, Davis L (2017) Two-stream neural networks for tampered face detection. Proc. CVPR. Workshops, pp 1831–1839
Acknowledgments
This work is supported in part by the National Key Research & Development Plan (2018YFB1003205) and National Natural Science Foundation of China (61972143, 61972142).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Guo, Z., Hu, L., Xia, M. et al. Blind detection of glow-based facial forgery. Multimed Tools Appl 80, 7687–7710 (2021). https://doi.org/10.1007/s11042-020-10098-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-10098-y