Fast projections of spatial rich model feature for digital image steganalysis

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.

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