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A proactive secret image sharing scheme with resistance to machine learning based steganalysis

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Abstract

In secret image sharing (SIS) schemes, a secret image is shared among a set of n images called stego-images. Each stego-image is preserved by a participant. In the recovery stage, at least k out of n stego-images are required to obtain the secret image, while k − 1 cannot reveal the secret in the sense of perfect secrecy. Hence, SIS guarantees long-term security. However, as the longer the stego-images remain stored, the higher is the probability of being vulnerable against steganalysis. To resolve this issue, this paper proposes the use of proactive secret sharing in an SIS scheme (P-SIS). P-SIS allows the stego-images to be renewed frequently while these are stored, without changing both cover and secret images. However, direct implementation of a proactive SIS requires more embedding rate (ER), causing high steganalysis accuracy detection and loss of quality in the stego-images. Our proposal addresses this issue and presents the combination of a (k, L, n)-threshold ramp secret sharing scheme and least significant bit matching (LSBM) steganography to reduce the steganalysis accuracy detection. The results of the evaluation show effectiveness of the proposal in terms of good quality of the stego-images, accurate recovery of the secret, and reduce the ER. Note that, despite the extensive research of SIS presented until now, only a few previous work is found on steganalysis in SIS. Not only constructing P-SIS scheme, but we also experimented the tolerance of the proposed P-SIS scheme against stganalysis in this paper. As a result, it is shown that the proposed scheme can withstand steganalysis based on machine learning (i.e., based on subtractive pixel adjacency matrix, SPAM).

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Acknowledgements

This work was partially supported by JSPS KAKENHI Grant Number JP26420345 and CONACYT (Consejo Nacional de Ciencia y Tecnología / National Council for Science and Technology).

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Authors and Affiliations

  1. Department of Informatics, Graduate School of Informatics and Engineering, The University of Electro-Communications, 1–5–1 Chofugaoka, Chofu, Tokyo, 182–8585, Japan

    Angelina Espejel-Trujillo & Mitsugu Iwamoto

  2. Postgraduate and Research Section, Mechanical Electrical Engineering School, Instituto Politecnico Nacional, Av. Santa Ana no. 1000, Col. San Francisco Culhuacan, Del. Coyoacan, Mexico City, Mexico

    Mariko Nakano-Miyatake

Authors
  1. Angelina Espejel-Trujillo
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  2. Mitsugu Iwamoto
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  3. Mariko Nakano-Miyatake
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Correspondence to Angelina Espejel-Trujillo.

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Espejel-Trujillo, A., Iwamoto, M. & Nakano-Miyatake, M. A proactive secret image sharing scheme with resistance to machine learning based steganalysis. Multimed Tools Appl 77, 15161–15179 (2018). https://doi.org/10.1007/s11042-017-5097-8

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  • DOI: https://doi.org/10.1007/s11042-017-5097-8

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