Steganalysis using modified pixel comparison and complexity measure

This article presents a new approach, which focuses on the following problems: detection and localization of stego informative regions within digital clean and noisy images; removing hidden data along with minimizing the statistical differences between stego images and stego information removed image. The new approach is based on a new pixel comparison and a new complexity measure. This new measure identifies the informative and stego-like regions of an image, with the objective of stegoanalysis through the saving of informative regions and the discarding of stego-like areas. The areas that are harder for detection are scanned in an alternate method in an attempt to detect areas that are classified as good for embedding. This allows for a higher detection rate and a low false positive. Experimental results will be presented in the complete write up. The data gathered will be listed on tables from a set of 100+ digital images. The images used in the analysis will vary in size, format, and color. Various commonly employed (e.g., S-Tools, SecurEngine, and wbStego3.51) approaches were used to hide hidden data onto the digital images for analysis. The new method has shown remarkable detection accuracy and localization of embedded information for LSB embedding. We have also shown that the presented method works even in the presence of noise in the image. In addition, this method shows that an image can be divided into ideal detection areas and ideal embedding areas. With this in mind the image can be scanned for the ideal detection methods to reduce both false positives and false negatives. This technique can be applied to data compression and for hiding secret information, in both time and transformed domains. It is also independent of the order color vectors in the palette.