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

Angelina Espejel-Trujillo, Mitsugu Iwamoto, Mariko Nakano-Miyatake

Research output: Contribution to journalArticle

Abstract

© 2017, Springer Science+Business Media, LLC. 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).
Original languageAmerican English
Pages (from-to)15161-15179
Number of pages13643
JournalMultimedia Tools and Applications
DOIs
StatePublished - 1 Jun 2018

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Learning systems
Recovery
Steganography
Pixels
Industry

Cite this

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title = "A proactive secret image sharing scheme with resistance to machine learning based steganalysis",
abstract = "{\circledC} 2017, Springer Science+Business Media, LLC. 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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A proactive secret image sharing scheme with resistance to machine learning based steganalysis. / Espejel-Trujillo, Angelina; Iwamoto, Mitsugu; Nakano-Miyatake, Mariko.

In: Multimedia Tools and Applications, 01.06.2018, p. 15161-15179.

Research output: Contribution to journalArticle

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