A new metaheuristic feature subset selection approach for image steganalysis
Processing a huge amount of information takes extensive execution time and computational sources most of the time with low classification accuracy. As a result, it is needed to employ a phase of pre-analysis processing, which can influence the performance of execution time and computational sources and classification accuracy. One of the most important phases of pre-processing is Feature selection, which can improve the classification accuracy of steganalysis. The experiments are accomplished by using a large and important data set of 686 features vectores named SPAM. One of the promising application domains for such a feature selection process is steganalysis. In this paper, we propose a new metaheuristic approach for image steganalysis method for detecting stego images from the cover images in JPEG images using a feature selection technique based on an improved artificial bee colony. Within the ABC structure the k-Nearest Neighbor (kNN) method is employed for fitness evaluation. ABC and kNN have been adjusted together to make an operative dimension reduction method Experimental results demonstrate the effectiveness and accuracy of the proposed technique compared to recent ABC-based feature selection methods and other existing techniques.