Compared with the process of embedding, the image contents make a more significant impact on the differences of image statistical characteristics. This makes the image steganalysis to be a classification problem with bigger withinclass scatter distances and smaller between-class scatter distances. As a result, the steganalysis features will be inseparate caused by the differences of image statistical characteristics. In this paper, a new steganalysis framework which can reduce the differences of image statistical characteristics caused by various content and processing methods is proposed. The given images are segmented to several sub-images according to the texture complexity. Steganalysis features are separately extracted from each subset with the same or close texture complexity to build a classifier. The final steganalysis result is figured out through a weighted fusing process. The theoretical analysis and experimental results can demonstrate the validity of the framework.
Least significant bit matching revisited (LSBMR) steganography uses two adjacent pixels as an embedding unit to conceal secret messages. In each unit, the modification probability of the first pixel is twice the modification probability of the second pixel, thereby causing an asymmetric effect on the statistical distribution of pixel difference in LSBMR for consecutive pixels (LSBMRCP). On the basis of the analysis of this asymmetric effect, a fast steganalytic feature is deduced and an embedding rate estimating method that employs an iteration strategy is proposed. Experimental results show that the steganalytic feature can effectively detect LSBMRCP steganography and that the detection performance is superior to the methods proposed by Tan and Xiong et al. Moreover, the embedding rate estimating method is so accurate that the order of magnitude of prediction error is maintained at 10 −2 , measured by the mean absolute error, median absolute difference, and interquartile range, thus indicating that the proposed method significantly outperforms the method proposed by Tan.
The current JPEG steganalysis systems, which include various strategies for feature extraction, have attained outstanding achievements. However, a common shortcoming is that they are always conducted on the entire image and do not take advantage of the content diversity. In addition, compared with a low-dimensional feature set, an appropriate rich model with high-dimensional features can obtain substantial improvement in steganalysis performances. A new steganalysis algorithm based on image segmentation is proposed which enables us to utilize the content characteristics of JPEG images. The given images are segmented to several subimages according to the texture complexity and then high-dimensional steganalysis features of each sort of subimages with the same or close texture complexity are extracted separately to build a classifier. The steganalysis results of the whole image are figured out through a weighted fusing process of all categories of the subimages. Experimental results demonstrate that the proposed method exhibits excellent performances and improves the detection accuracy.