Content‐adaptive steganalysis for color images

Abstract

Some steganography methods for gray-scale image can be extended to true RGB color image by treating each of its three color channels as a gray-scale image. In modern popular steganography, most embedding changes are highly concentrated on those complex textural regions with smaller embedding distortions. However, the existing steganalysis methods for color images directly extract steganalytic features from the whole image. In this paper, we propose a content-adaptive steganalysis strategy for color images. The new strategy aims to extract spatial rich model features from each color channel and just extract color rich model features from the pixels that may have been modified. In order to locate those suspected pixels, we first calculate the embedding costs of each channel, and then a subset of pixels with smaller embedding costs is selected. Experimental results show that the proposed strategy performs better than the state-of-the-art color image steganalysis method.

Color image steganalysis based on embedding change probabilities in differential channels

“…In recent years, researchers have proposed many steganalysis algorithms for color image steganography. 15–27 The steganalysis features used in these algorithms significantly reduce the detection error for color images. However, these features do not consider that the content-adaptive steganography changes the pixels in complex textured regions with higher probabilities.…”

Color Image Steganalysis Based on Residuals of Channel Differences

“…And for color image steganography, some steganalysis algorithms have also been proposed. Here, they are classified into various types, including steganalysis algorithms based on changes of color number [Fridrich, Du and Long (2000); Su, Han, Huang et al (2011)], steganalysis algorithms based on inter-channel texture consistency [Abdulrahman, Chaumont, Montesinos et al (2016b)], steganalysis algorithms based on co-occurrence matrices across channels [Goljan, Fridrich and Cogranne (2014); Goljan and Fridrich (2015); Liao, Chen and Yin (2016)], steganalysis algorithms based on inter-channel prediction errors [Lyu and Farid (2004); Liu, Sung, Xu et al (2006); Li, Zhang and Yu (2014)], and steganalysis algorithms based on combinations of different channel features [Abdulrahman, Chaumont, Montesinos et al (2016a)]. The steganalysis algorithms based on changes of color number primarily used the characteristic that steganography will increase the number of colors or similar color pairs to detect color stego images.…”

“…Goljan et al [Goljan and Fridrich (2015)] divided the image pixels into blocks according to the color filter array characteristics from the imaging principle of a camera, and then computed the co-occurrence matrices across residuals in different channels from each block and merged them as the final feature set for steganalysis. Liao et al [Liao, Chen and Yin (2016)] obtained the regions with complex texture in each channel and the regions with complex texture in any channel, and then calculated the cooccurrence matrices from residuals of these two types of regions in each channel and combined them as steganalysis features, that improved the detection accuracy of new adaptive steganography, such as WOW and S-UNIWARD. The steganalysis algorithms based on inter-channel prediction errors considered the correlation between channels when calculating the prediction errors of the image elements (such as pixel or wavelet coefficients) or their features, and then combined the features of prediction errors with other features to detect the color stego image.…”

Color image steganalysis based on channel gradient correlation

“…Goljan and Fridrich 20 divided the image pixels into blocks according to the color filter array (CFA) characteristics from the imaging principle of camera and then computed the co-occurrence matrices of residuals between different channels of each block for steganalysis. According to the characteristics that contentadaptive steganography embeds information into complex textural regions, Liao et al 21 first obtained the complex texture regions in all channels and in each channel, respectively, and then calculated the cooccurrence matrices of residuals in each channel of the two types of regions as steganalysis features. Lyu and Farid 22 calculated logarithmic prediction errors from the correlations among wavelet subband coefficients of different scales and different color channels in horizontal, vertical, and diagonal directions, respectively, and extracted their statistic features for steganalysis, such as mean, variance, skewness, and kurtosis, which achieves the pure blind detection of color image steganography.…”