Application of genetic algorithm to steganalysis
Timothy Knapik, Ephraim Lo, John A. Marsh · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
We present a novel application of genetic algorithm (GA) to optimal feature set selection in supervised learning using support vector machine (SVM) for steganalysis. Steganalysis attempts to determine whether a cover object (in our case an image file) contains hidden information. This is a bivariate classification problem: the image either does or does not contain hidden data. Our SVM classifier uses a training set of images with known classification to "learn" how to classify images with unknown classification. The SVM uses a feature set, essentially a set of statistical quantities extracted from the image. The performance of the SVM classifier is heavily dependent on the feature set used. Too many features not only increase computation time but decrease performance, and too few features do not provide enough information for accurate classification. Our steganalysis technique uses entropic features that yield up to 240 features per image. The selection of an optimum feature set is a problem that lends itself well to genetic algorithm optimization. We describe this technique in detail and present a "GA optimized" feature set of 48 features that, for our application, optimizes the tradeoff between computation time and classification accuracy.