Evolving GA Classifier for Breaking the Steganographic Utilities: Stools, Steganos and Jsteg

Differentiating anomalous image documents (stego image) from pure image file (cover image) is difficult and tedious. Steganalytic techniques strive to detect whether an image contains a hidden message or not. This paper presents a genetic algorithm (GA) based approach to image steganalysis. The basic idea is that, the various image quality metrics calculated on cover image files and on stego-image files vis-a-vis their denoised versions, are statistically different. GA is employed to derive a set of classification rules from image data using these image quality metrics and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify the image documents in a real-time environment. Unlike most existing GA-based approaches, because of the simple representation of rules and the effective fitness function, the proposed method is easier to implement while providing the flexibility to generally detect any new steganography technique. The implementation of the GA based image steganalyzer relies on the choice of these image quality metrics and the construction of a two-class classifier, which will discriminate between the adulterated and the untouched image samples. Experimental results show that the proposed technique provides promising detection rates.