Abstract
In recent years, deep neural network is active in the field of computer image vision. The existing digital watermarking technology based on deep neural network can resist image attacks, but image quality is not satisfied. In order to improve the quality of the watermarked images, a neural network watermarking method based on image texture analysis is proposed. Firstly, image texture features are analyzed by gray co-occurrence matrix, and the image is divided into texture complex region and flat region. Secondly, in order to reduce the degree of image modification for better quality, StegaStamp network is adopted to embed the watermark into the flat texture area. Finally, from the perspective of traditional multiplicative watermarking embedding, the watermark embedding process of deep neural network is improved to enhance the watermarked image quality. Experimental results show that the proposed method can effectively improve the quality of the watermarked images without degrading the robustness.
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
References
Wason, R., Jain, V., Narula, G.S., Balyan, A.: Deep understanding of 3-D multimedia information retrieval on social media: implications and challenges. Iran J. Comput. Sci. 2(2), 101–111 (2019)
Zhang, H., Wei, Z.: Risk management of commodity trade business based on deep learning and parallel processing of visual multimedia big data. Multimedia Tools Appl. 79(13), 9331–9349 (2019). https://doi.org/10.1007/s11042-019-7508-5
Dou, K., Guo, B., Kuang, L.: A privacy-preserving multimedia recommendation in the context of social network based on weighted noise injection. Multimedia Tools Appl. 78(19), 26907–26926 (2017). https://doi.org/10.1007/s11042-017-4352-3
Cox, I.: Digital watermarking. J. Electron. Imaging 11(3), 414 (2002)
Podilchuk, C.I., Delp, E.J.: Digital watermarking: algorithms and applications. IEEE Signal Process. Mag. 18(4), 33–46 (2001)
Xia, Z., Wang, X., Sun, X., Liu, Q., Xiong, N.: Steganalysis of LSB matching using differences between nonadjacent pixels. Multimedia Tools Appl. 75(4), 1947–1962 (2014). https://doi.org/10.1007/s11042-014-2381-8
Luo, W., Huang, F., Huang, J.: Edge adaptive image steganography based on LSB matching revisited. IEEE Trans. Inf. Forensics Secur. 5(2), 201–214 (2010)
Barni, M., Bartolini, F., Cappellini, V.: A DCT-domain system for robust image watermarking. Sig. Process. 66(3), 357–372 (1998)
Shi, Y.Q.: Reversible data hiding. In: Cox, I.J., Kalker, T., Lee, H.-K. (eds.) IWDW 2004. LNCS, vol. 3304, pp. 1–12. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31805-7_1
Shi, Y.Q., Ansari, N., Su, W., Ni, Z.: Reversible data hiding. IEEE Trans. Circuits Syst. Video Technol. 16(3), 354–362 (2006)
Fazli, S., Moeini, M.: A robust image watermarking method based on DWT, DCT, and SVD using a new technique for correction of main geometric attacks. Optik-Int. J. Light Electron Opt. 127(2), 964–972 (2016)
Shi, H., Dong, J., Wang, W., Qian, Y., Zhang, X.: SSGAN: secure steganography based on generative adversarial networks. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds.) PCM 2017. LNCS, vol. 10735, pp. 534–544. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77380-3_51
Mun, S.M., Nam, S.H., Jang, H.U., Kim, D., Lee, H.K.: A robust blind watermarking using convolutional neural network. arXiv:1704.03248 [cs.MM] (2017)
Zhu, J., Kaplan, R., Johnson, J., Fei-Fei, L.: HiDDeN: hiding data with deep networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 682–697. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_40
Matthew, T., Ben, M., Ng, R.: StegaStamp: invisible hyperlinks in physical photographs. arXiv preprint arXiv:1904.05343 (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. In: Neural Information Processing Systems (2015)
Cheng, Q., Huang, T.S.: Robust optimum detection of transform domain multiplicative watermarks. IEEE Trans. Signal Process. 51(4), 906–924 (2003)
Sadreazami, H., Ahmad, M.O., Swamy, M.N.S.: Multiplicative watermark decoder in contourlet domain using the normal inverse gaussian distribution. IEEE Trans. Multimedia 18(2), 196–207 (2015)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning (2017)
Huang, Z.K., Li, P.W., Hou, L.Y.: Segmentation of textures using PCA fusion based Gray-Level Co-occurrence Matrix features. In: International Conference on Test and Measurement, Hong Kong, pp. 103–105 (2009)
Baraldi, A., Parmiggiani, F.: An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Trans. Geosci. Remote Sens. 33(2), 293–304 (1995)
Kanopoulos, N., Vasanthavada, N., Baker, R.L.: Design of an image edge detection filter using the sobel operator. IEEE J. Solid-State Circuits 23(2), 358–367 (1988)
Kuppili, S.K., Prasad, P.M.K.: Design of area optimized sobel edge detection. In: Jain, L.C., Behera, H.S., Mandal, J.K., Mohapatra, D.P. (eds.) Computational Intelligence in Data Mining - Volume 2. SIST, vol. 32, pp. 647–655. Springer, New Delhi (2015). https://doi.org/10.1007/978-81-322-2208-8_59
Huiskes, M.J., Lew, M.S.: The MIR flickr retrieval evaluation. In: ACM International Conference on Multimedia Information Retrieval (MIR 2008), Vancouver, Canada (2008)
Bose, R.C., Raychaudhuri, D.K.: On a class of error correcting binary group codes. Inf. Control 3(1), 68–79 (1960)
Huynh-Thu, Q., Ghanbari, M.: Scope of validity of PSNR in image/video quality assessment. Electron. Lett. 44(13), 800–801 (2008)
Soleimanizadeh, S., Mohamad, D., Saba, T., Rehman, A.: Recognition of partially occluded objects based on the three different color spaces (RGB, YCbCr, HSV). 3D Res. 6(3), 22 (2015). https://doi.org/10.1007/s13319-015-0052-9
Acknowledgments
This work was partially supported by National Natural Science Foundation of China (No. 61971247, No. 61370218), and Public Welfare Technology and Industry Project of Zhejiang Provincial Science Technology Department (No. LGG19F020016).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, K., Li, L., Luo, T., Chang, CC. (2020). Deep Neural Network Watermarking Based on Texture Analysis. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1252. Springer, Singapore. https://doi.org/10.1007/978-981-15-8083-3_50
Download citation
DOI: https://doi.org/10.1007/978-981-15-8083-3_50
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8082-6
Online ISBN: 978-981-15-8083-3
eBook Packages: Computer ScienceComputer Science (R0)