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Soft Computing

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14.01.2016 | Methodologies and Application

Fast projections of spatial rich model feature for digital image steganalysis

verfasst von: Pengfei Wang, Zhihui Wei, Liang Xiao

Erschienen in: Soft Computing | Ausgabe 12/2017

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Abstract

Spatial rich model (SRM) is a classic steganalysis method, which collects high-order co-occurrences from truncated noise residuals as feature to capture the local-range dependencies of an image. Increasing the truncation threshold and the co-occurrence order will lead to a higher-dimensional feature, which can exploit more statistical bins and capture dependencies across larger-range neighborhood, but this will suffer from the curse of dimensionality. In this paper, we propose a fast projection method to increase the statistical robustness of the higher-dimensional SRM feature while decreasing its dimensionality. The proposed projection method is applicable to co-occurrence-based steganalysis features. The detection performance and the computational complexity of the proposed method are investigated on three content-adaptive steganographic algorithms in spatial domain.

Vorheriger Artikel Efficient pattern classification model with neuro-fuzzy networks

Nächster Artikel An analysis of multi-dimensional data containing emphasized items by self-organizing map and its application to sightseeing information analysis

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Titel: Fast projections of spatial rich model feature for digital image steganalysis
verfasst von: Pengfei Wang
Zhihui Wei
Liang Xiao
Publikationsdatum: 14.01.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 12/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-015-2011-z

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