Abstract
Due to the introduction of deep learning for text detection and recognition in natural scenes, and the increase in detecting fake images in crime applications, automatically generating fake character images has now received greater attentions. This paper presents a new system named Fake Character GAN (FCGAN). It has the ability to generate fake and artificial scene characters that have similar shapes and colors with the existing ones. The proposed method first extracts shapes and colors of character images. Then, it constructs the FCGAN, which consists of a series of convolution, residual and transposed convolution blocks. The extracted features are then fed to the FCGAN to generate fake characters and verify the quality of the generated characters simultaneously. The proposed system chooses characters from the benchmark ICDAR 2015 dataset for training, and further validated by conducting text detection and recognition experiments on input and generated fake images to show its effectiveness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Farid, H.: Image forgery detection. IEEE Signal Process. Mag. 26(2), 16–25 (2009)
Jaderberg, M., et al.: Synthetic data and artificial neural networks for natural scene text recognition (2014). arXiv preprint: arXiv:1406.2227
Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro (2017). arXiv preprint: arXiv:1701.07717
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (2014)
Zhu, J.-Y., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks (2017). arXiv preprint: arXiv:1703.10593
Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and locally consistent image completion. ACM Trans. Graph. (TOG) 36(4), 107 (2017)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN (2017). arXiv preprint: arXiv:1701.07875
Mao, X., et al.: Least squares generative adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE (2017)
Denton, E.L., Chintala, S., Fergus, R.: Deep generative image models using a Laplacian pyramid of adversarial networks. In: Advances in Neural Information Processing Systems (2015)
Zhu, J.-Y., Krähenbühl, P., Shechtman, E., Efros, A.A.: Generative visual manipulation on the natural image manifold. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part V. LNCS, vol. 9909, pp. 597–613. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_36
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks (2015). arXiv preprint: arXiv:1511.06434
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network (2016). arXiv preprint: arXiv:1609.04802
Salimans, T., et al.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems (2016)
Conotter, V., Boato, G., Farid, H.: Detecting photo manipulation on signs and billboards. In: 2010 17th IEEE International Conference on Image Processing (ICIP). IEEE (2010)
Abramova, S.: Detecting copy-move forgeries in scanned text documents. Electron. Imaging 2016(8), 1–9 (2016)
Isola, P., et al.: Image-to-image translation with conditional adversarial networks (2016). arXiv preprint: arXiv:1611.07004
Reed, S., et al.: Generative adversarial text to image synthesis (2016). arXiv preprint: arXiv:1605.05396
Liu, M.-Y., Tuzel, O.: Coupled generative adversarial networks. In: Advances in Neural Information Processing Systems (2016)
Liu, M.-Y., Breuel, T., Kautz, J.: Unsupervised Image-to-Image Translation Networks (2017). arXiv preprint: arXiv:1703.00848
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Kingma, D., Ba, J.: Adam: A method for stochastic optimization (2014). arXiv preprint: arXiv:1412.6980
Karatzas, D., et al.: ICDAR 2015 competition on robust reading. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE (2015)
Tian, Z., Huang, W., He, T., He, P., Qiao, Y.: Detecting text in natural image with connectionist text proposal network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 56–72. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_4
Zhou, X., et al.: EAST: An Efficient and Accurate Scene Text Detector (2017). arXiv preprint: arXiv:1704.03155
Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298–2304 (2017)
Acknowledgment
This work was supported by the Natural Science Foundation of China under Grant 61672273, Grant 61832008 and Grant 61702160, the Science Foundation for Distinguished Young Scholars of Jiangsu under Grant BK20160021, Scientific Foundation of State Grid Corporation of China (Research on Ice-wind Disaster Feature Recognition and Prediction by Few-shot Machine Learning in Transmission Lines), National Key R&D Program of China under Grant 2018YFC0407901, the Fundamental Research Funds for the Central Universities under Grant 2016B14114, the Science Foundation of JiangSu under Grant BK20170892, and the open Project of the National Key Lab for Novel Software Technology in NJU under Grant K-FKT2017B05.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yue, Y., Shivakumara, P., Wu, Y., Zhu, L., Lu, T., Pal, U. (2019). An Automatic System for Generating Artificial Fake Character Images. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11296. Springer, Cham. https://doi.org/10.1007/978-3-030-05716-9_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-05716-9_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05715-2
Online ISBN: 978-3-030-05716-9
eBook Packages: Computer ScienceComputer Science (R0)