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A Coverless Image Information Hiding Algorithm Based on Fractal Theory

Xiang Zhang

College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, P. R. China

E-mail Address: martin_zx@hnu.edu.cn

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Fei Peng

http://orcid.org/0000-0001-8053-4587

College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, P. R. China

E-mail Address: eepengf@gmail.com

Corresponding author

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Zixing Lin

College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, P. R. China

E-mail Address: b1410z0225@hnu.edu.cn

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, and

Min Long

College of Computer and Communication Engineering, Changsha University of Science and Technology, Hunan University, Changsha 410114, P. R. China

E-mail Address: caslongm@aliyun.cn

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https://doi.org/10.1142/S0218127420500625Cited by:6 (Source: Crossref)

Abstract

To improve the robustness and imperceptibility of the existing coverless image information hiding, a generative coverless image information hiding algorithm based on fractal theory is proposed in this paper. Firstly, four fractal image generation methods are analyzed, and the relationship between the coverless information hiding and these methods is discussed. Secondly, based on the fractal image generation algorithm, secret information is hidden by controlling pixel rendering during the generation process. The robustness, imperceptibility, and capability of resisting steganalysis are balanced by adjusting the rendering distance. As it directly generates stego images, this can resist the detection of most existing steganalysis methods. Meanwhile, different capacities can be achieved by adjusting the size of the generated image. Experimental results and analysis show that the proposed scheme can effectively resist steganalysis and has good robustness against various image attacks. Furthermore, it can achieve large capacity, and it has broad prospects for covert communication.

Keywords:

References

Agaian, S. S. & Susmilch, J. M. [2006] “ Fractal steganography using artificially generated images,” 2006 IEEE Region 5 Conf. (IEEE), pp. 312–317. Crossref, Google Scholar
Akhaee, M. A., Saberian, M. J., Feizi, S. & Marvasti, F. [2009] “ Robust audio data hiding using correlated quantization with histogram-based detector,” IEEE Trans. Multimed. 11, 834–842. Crossref, Web of Science, Google Scholar
Barnsley, M. F., Devaney, R. L., Mandelbrot, B. B., Peitgen, H.-O., Saupe, D. & Voss, R. F. [1988] The Science of Fractal Images (Springer). Crossref, Google Scholar
Bouallegue, K. [2015] “ Gallery of chaotic attractors generated by fractal network,” Int. J. Bifurcation and Chaos 25, 1530002-1–18. Link, Web of Science, Google Scholar
Cao, Y., Zhou, Z., Sun, X. & Gao, C. [2018a] “ Coverless information hiding based on the molecular structure images of material,” Comput. Mater. Contin. 54, 197–207. Web of Science, Google Scholar
Cao, Y., Zhou, Z., Yang, C.-N. & Sun, X. [2018b] “ Dynamic content selection framework applied to coverless information hiding,” J. Internet Technol. 19, 1179–1186. Web of Science, Google Scholar
Chen, X., Sun, H., Tobe, Y., Zhou, Z. & Sun, X. [2015] “ Coverless information hiding method based on the Chinese mathematical expression,” Int. Conf. Cloud Computing and Security (Springer), pp. 133–143. Crossref, Google Scholar
Duan, X., Song, H., Qin, C. & Khan, M. K. [2018] “ Coverless steganography for digital images based on a generative model,” Comput. Mater. Contin. 55, 483–493. Web of Science, Google Scholar
Eswari, G. S., Leelavathy, N. & Rani, U. S. [2014] “ Fractal image steganography using nonlinear model,” Int. J. Innov. Res. Comput. Commun. Engin. 2, 2644–2649. Google Scholar
Fridrich, J. & Kodovsky, J. [2012] “ Rich models for steganalysis of digital images,” IEEE Trans. Inform. Forens. Secur. 7, 868–882. Crossref, Web of Science, Google Scholar
Ge, X., Lu, B., Liu, F. & Gong, D. [2016] “ An image encryption algorithm based on information hiding,” Int. J. Bifurcation and Chaos 26, 1650192-1–14. Link, Web of Science, Google Scholar
He, Y., Yang, G. & Zhu, N. [2012] “ A real-time dual watermarking algorithm of h. 264/avc video stream for video-on-demand service,” AEU-Int. J. Electron. Commun. 66, 305–312. Crossref, Web of Science, Google Scholar
Holub, V., Fridrich, J. & Denemark, T. [2014] “ Universal distortion function for steganography in an arbitrary domain,” EURASIP J. Inform. Secur. 2014, 1. Crossref, Google Scholar
Hsieh, M.-S., Tseng, D.-C. & Huang, Y.-H. [2001] “ Hiding digital watermarks using multiresolution wavelet transform,” IEEE Trans. Industr. Electron. 48, 875–882. Crossref, Web of Science, Google Scholar
Kodovsky, J., Fridrich, J. & Holub, V. [2011] “ Ensemble classifiers for steganalysis of digital media,” IEEE Trans. Inform. Forens. Secur. 7, 432–444. Crossref, Web of Science, Google Scholar
Lee, W. K., Ong, S., Wong, K. & Tanaka, K. [2018] “ A novel coverless information hiding technique using pattern image synthesis,” 2018 Asia-Pacific Signal and Information Processing Association Ann. Summit and Conf. (APSIPA ASC) (IEEE), pp. 1122–1127. Crossref, Google Scholar
Li, C., Zhang, Y. & Xie, E. Y. [2019] “ When an attacker meets a cipher-image in 2018: A year in review,” J. Inform. Secur. Appl. 48, 102361. Web of Science, Google Scholar
Liao, X., Guo, S., Yin, J., Wang, H., Li, X. & Sangaiah, A. K. [2018] “ New cubic reference table based image steganography,” Multimed. Tools Appl. 77, 10033–10050. Crossref, Web of Science, Google Scholar
Lin, Z.-X., Peng, F. & Long, M. [2018] “ A low-distortion reversible watermarking for 2d engineering graphics based on region nesting,” IEEE Trans. Inform. Forens. Secur. 13, 2372–2382. Crossref, Web of Science, Google Scholar
Liu, M., Zhang, M., Liu, J. & Yang, X. [2018] “ Generative steganography based on GANs,” Int. Conf. Cloud Computing and Security (Springer), pp. 537–549. Crossref, Google Scholar
Lu, S., Jaffer, A., Jin, X., Zhao, H. & Mao, X. [2011] “ Mathematical marbling,” IEEE Comput. Graph. Appl. 32, 26–35. Web of Science, Google Scholar
Luo, W., Huang, F. & Huang, J. [2010] “ Edge adaptive image steganography based on LSB matching revisited,” IEEE Trans. Inform. Forens. Secur. 5, 201–214. Crossref, Web of Science, Google Scholar
Mandelbrot, B. [1975] Les Objets Fractals: Forme, Hasard et Dimension (Flammarion, Paris). Google Scholar
Merkle, R. & Hellman, M. [1978] “ Hiding information and signatures in trapdoor knapsacks,” IEEE Trans. Inform. Th. 24, 525–530. Crossref, Web of Science, Google Scholar
Nori, A. S. & Al-Qassab, A. M. [2012] “ Steganographic technique using fractal image,” Int. J. Inform. Technol. Busin. Manag. 23, 52–59. Google Scholar
Otori, H. & Kuriyama, S. [2007] “ Data-embeddable texture synthesis,” Int. Symp. Smart Graphics (Springer), pp. 146–157. Crossref, Google Scholar
Otori, H. & Kuriyama, S. [2009] “ Texture synthesis for mobile data communications,” IEEE Comput. Graph. Appl. 29, 74–81. Crossref, Web of Science, Google Scholar
Ou, B., Li, X. & Wang, J. [2016] “ High-fidelity reversible data hiding based on pixel-value-ordering and pairwise prediction-error expansion,” J. Vis. Commun. Image Represent. 39, 12–23. Crossref, Web of Science, Google Scholar
Patel, H. N., Khant, D. R. & Prajapati, D. [2017] “ Design of a color palette based image steganography algorithm for fractal images,” 2017 Int. Conf. Wireless Communications, Signal Processing and Networking (WiSPNET) (IEEE), pp. 2584–2589. Crossref, Google Scholar
Peng, F., Lin, Z.-X., Zhang, X. & Long, M. [2019] “ Reversible data hiding in encrypted 2D vector graphics based on reversible mapping model for real numbers,” IEEE Trans. Inform. Forens. Secur. 14, 2400–2411. Crossref, Web of Science, Google Scholar
Qian, Z., Zhou, H., Zhang, W. & Zhang, X. [2017] “ Robust steganography using texture synthesis,” Advances in Intelligent Information Hiding and Multimedia Signal Processing (Springer), pp. 25–33. Crossref, Google Scholar
Qian, Z., Pan, L., Li, S. & Zhang, X. [2018] “ Steganography by constructing marbling texture,” Int. Conf. Cloud Computing and Security (Springer), pp. 428–439. Crossref, Google Scholar
Qin, Z., Li, M. & Wu, B. [2017] “ Robust steganography via patch-based texture synthesis,” Int. Conf. Internet Multimedia Computing and Service (Springer), pp. 429–439. Google Scholar
Song, X., Liu, F., Yang, C., Luo, X. & Zhang, Y. [2015] “ Steganalysis of adaptive JPEG steganography using 2D Gabor filters,” Proc. 3rd ACM Workshop on Information Hiding and Multimedia Security (ACM), pp. 15–23. Crossref, Google Scholar
Su, Q. & Chen, B. [2018] “ Robust color image watermarking technique in the spatial domain,” Soft Comput. 22, 91–106. Crossref, Web of Science, Google Scholar
Sun, J., Liao, X., Chen, X. & Guo, S. [2017] “ Privacy-aware image encryption based on Logistic map and data hiding,” Int. J. Bifurcation and Chaos 27, 1750073-1–15. Link, Web of Science, Google Scholar
Wang, Z., Bovik, A. C., Sheikh, H. R., Simoncelli, E. P. et al. [2004] “ Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612. Crossref, Web of Science, Google Scholar
Wei, W., Chengfeng, A., Wang, L. & Ma, H. [2018] “ A texture synthesis steganography scheme based on super-pixel structure and SVM,” Int. Conf. Intelligent Information Processing (Springer), pp. 375–383. Crossref, Google Scholar
Winkler, S. & Mohandas, P. [2008] “ The evolution of video quality measurement: From PSNR to hybrid metrics,” IEEE Trans. Broadcasting 54, 660–668. Crossref, Web of Science, Google Scholar
Wu, K.-C. & Wang, C.-M. [2014] “ Steganography using reversible texture synthesis,” IEEE Trans. Image Process. 24, 130–139. Web of Science, Google Scholar
Wu, J., Liu, Y., Dai, Z., Kang, Z., Rahbar, S. & Jia, Y. [2018] “ A coverless information hiding algorithm based on grayscale gradient co-occurrence matrix,” IETE Tech. Rev. 35, 23–33. Crossref, Web of Science, Google Scholar
Xu, J., Mao, X., Jin, X., Jaffer, A., Lu, S., Li, L. & Toyoura, M. [2015] “ Hidden message in a deformation-based texture,” Vis. Comput. 31, 1653–1669. Crossref, Web of Science, Google Scholar
Yang, Z.-L., Guo, X.-Q., Chen, Z.-M., Huang, Y.-F. & Zhang, Y.-J. [2019] “ RNN-stega: Linguistic steganography based on recurrent neural networks,” IEEE Trans. Inform. Forens. Secur. 14, 1280–1295. Crossref, Web of Science, Google Scholar
Yuan, C., Xia, Z. & Sun, X. [2017] “ Coverless image steganography based on SIFT and BOF,” J. Internet Technol. 18, 435–442. Web of Science, Google Scholar
Zhang, H., Hu, J., Wang, G. & Zhang, Y. [2011] “ A steganography scheme based on fractal images,” 2011 2nd Int. Conf. Networking and Distributed Computing (IEEE), pp. 28–31. Crossref, Google Scholar
Zhang, X., Peng, F. & Long, M. [2018] “ Robust coverless image steganography based on DCT and LDA topic classification,” IEEE Trans. Multimed. 20, 3223–3238. Crossref, Web of Science, Google Scholar
Zheng, S., Wang, L., Ling, B. & Hu, D. [2017] “ Coverless information hiding based on robust image hashing,” Int. Conf. Intelligent Computing (Springer), pp. 536–547. Crossref, Google Scholar
Zhou, Z., Sun, H., Harit, R., Chen, X. & Sun, X. [2015] “ Coverless image steganography without embedding,” Int. Conf. Cloud Computing and Security (Springer), pp. 123–132. Crossref, Google Scholar
Zhou, Z., Wu, Q. J., Yang, C.-N., Sun, X. & Pan, Z. [2017] “ Coverless image steganography using histograms of oriented gradients-based hashing algorithm,” J. Internet Technol. 18, 1177–1184. Web of Science, Google Scholar
Zhou, Q., Qiu, Y., Li, L., Lu, J., Yuan, W., Feng, X. & Mao, X. [2018] “ Steganography using reversible texture synthesis based on seeded region growing and LSB,” Comput. Mater. Contin. 55, 151–163. Crossref, Web of Science, Google Scholar
Zhou, Z., Mu, Y. & Wu, Q. J. [2019] “ Coverless image steganography using partial-duplicate image retrieval,” Soft Comput. 23, 4927–4938. Crossref, Web of Science, Google Scholar
Zou, L., Sun, J., Gao, M., Wan, W. & Gupta, B. B. [2019] “ A novel coverless information hiding method based on the average pixel value of the sub-images,” Multimed. Tools Appl. 78, 7965–7980. Crossref, Web of Science, Google Scholar