Algorithms for Digital Image Steganography via Statistical Restoration
Gabriel Hospodar, Ingrid M.R. Verbauwhede, José Gabriel R. C. Gomes · 2012
Steganography is concerned with hiding information without raising any suspicion about the existence of such information. Applications of steganography typically involve security. We consider that information is embedded into Discrete Cosine Transform (DCT) coefficients of 8 × 8-pixel blocks in a natural digital image. The apparent absence of the hidden information is guaranteed by a compensation method that is applied after the hiding process. The compensation method aims at restoring the original statistics, e.g. probability mass function, of the DCT coefficients from the cover image, as Sarkar and Manjunath have done in [1]. In this work we propose three alternative steganographic approaches for the hiding and compensation processes based on [1]. Our embedding processes automatically perform part of the statistical restoration before the compensation process. We also propose an intuitive histogram-based compensation method. Its operation is similar to filling the bins of a histogram with a liquid, as if this liquid corresponds to probability flowing from the bins with excess of probability to the bins with deficit of probability. Classifiers based on artificial neural networks are trained to distinguish original (cover) and information-bearing (stego) images. The results show that we imperceptibly hide 8.3% more information than [1] by combining one of our hiding processes with the histogram-based compensation method used in [1]. The peak signal-to-noise ratio between the compensated stego and cover images is close to 37 dB.