Image Generation Method Based on the Recoverable Byte Sequence using the Neural Networks

Abstract

Due to the rapid development of information technologies, the tasks of ensuring information integrity, safety and confidentiality, as well as the possibility of guaranteed confirmation of its source, are becoming more relevant than ever. One of the possible solutions to this problem could be the steganographic methods, which allow both hiding the fact of information transfer and imperceptibly adding the useful data. Scientific literature describes a large number of steganographic algorithms. However, only insignificant number of works is devoted to the data hiding methods using the neural networks, and even less are devoted to generating containers for them. A method for generating images based on the hidden information is proposed, which guarantees possibility of both hiding and subsequent extraction of information eliminating the need to select an appropriate container. As part of the method, an algorithm was developed that included description of the stages of input data preprocessing, transformation into the container image and extraction of the hidden information. Examples of the proposed method operation are provided. The method could serve both as a steganographic algorithm for hiding information and as the algorithm for adding information in the form of watermarks

Please cite this article in English as:

Rudakov I.V., Filippov M.V., Kudryavtsev M.A. Image generation method based on the recoverable byte sequence using the neural networks. Herald of the Bauman Moscow State Technical University, Series Instrument Engineering, 2023, no. 1 (142), pp. 83--97 (in Russ.). DOI: https://doi.org/10.18698/0236-3933-2023-1-83-97

References

[1] Gribunin V.G., Turintsev I.V., Okov I.N. Tsifrovaya steganografiya [Digital steganography]. Moscow, Solon-Press Publ., 2009.

[2] Konakhovich G.F., Puzyrenko A.Yu. Kompyuternaya steganografiya. Teoriya i praktika [Computer steganography. Theory and practice]. Kiev, MK-Press Publ., 2006.

[3] Das S., Das S., Bandyopadhya B., et al. Steganography and steganalysis: different approaches. IJCITAE, 2008, vol. 2, no. 1. DOI: https://doi.org/10.48550/arXiv.1111.3758

[4] Nashat D., Mamdouh L. An efficient steganographic technique for hiding data. J. Egypt Math. Soc., 2019, vol. 27, pp. 57--71. DOI: https://doi.org/10.1186/s42787-019-0061-6

[5] Kaur H., Ran J. A survey on different techniques of steganography. MATEC Web Conf., 2016, vol. 57, art. 02003. DOI: https://doi.org/10.1051/matecconf/20165702003

[6] Gribunin V.G., Okov I.N., Turintsev I.V. Tsifrovaya steganografiya [Digital steganography]. Moscow, Solon-Press Publ., 2002.

[7] Zhao J., Koch E. Embedding robust labels into images for copyright protection. Proc. Int. Cong. on Intellectual Property Rights for Specialized Information, Knowledge and New Techniques, 1995, pp. 242--251.

[8] Korostil Yu.M., Shelest M.E. [Design principles of steganographic systems with structural technology]. Tr. VII Mezhdunar. konf. "Avtomatika-2000". Ch. 1 [Proc. VII Int. Conf. Automatics-2000. P. 1]. Lvov, DND1P Publ., 2000, pp. 273--286 (in Russ.).

[9] Joshi K., Gill S., Yadav R. A new method of image steganography using 7th bit of a pixel as indicator by introducing the successive temporary pixel in the gray scale image. J. Comput. Netw. Commun., 2018, vol. 2018, art. 9475142. DOI: https://doi.org/10.1155/2018/9475142

[10] Tiwari R.K., Sahoo G. Some new methodologies for image hiding using steganographic techniques. Cryptography and Security. DOI: https://doi.org/10.48550/arXiv.1211.0377

[11] Bandyopadhyay S.K. Advisor to chancellor. A proposed method for image steganography. Res. Med. Eng. Sci., 2018, vol. 3, no. 4, art. RMES.000569.2018. DOI: http://dx.doi.org/10.31031/rmes.2018.03.000569

[12] Hayes J., Danezis G. Generating steganographic images via adversarial training. Proc. NIPS, 2017, pp. 1951--1960.

[13] Zhang K.A., Cuesta-Infante A., Xu L., et al. SteganoGAN: high capacity image steganography with GANs. DOI: https://doi.org/10.48550/arXiv.1901.03892

[14] Dinh L., Sohl-Dickstein J., Bengio S. Density estimation using Real NVP. ICLR, 2017. DOI: https://doi.org/10.48550/arXiv.1605.08803

[15] Dinh L., Krueger D., Bengio Y. NICE: non-linear independent components estimation. ICLR, 2015. DOI: https://doi.org/10.48550/arXiv.1410.8516