Image steganalysis using improved particle swarm optimization based feature selection

Image steganalysis is the task of discovering the hidden message in a multimedia file in which the steganalysis technique highly depends on the feature elements of the image. Since there is a possibility for a feature vector to contain redundant elements, processing of redundant elements can be harmful in terms of long computation cost and large storage space. This paper proposes a new feature selection approach based on Adaptive inertia weight-based Particle Swarm Optimization (APSO) for the image steganalysis where the inertia weight of PSO is adaptively adjusted using the swarm diameter, average distance of particles around the center and average speed of particles towards the center. Also, the proposed APSO is used with the novel measure of Area Under the receiver operating characteristics Curve (AUC) as the fitness function to enhance the performance of identification of stego-images from the cover images in steganalysis problem. Due to appropriate convergence rate and the regulated search step of APSO, it is able to select the most significant and influential feature elements and so, the performance of steganalysis will be improved. Experimental results of the proposed method on Breaking Out Steganography System (BOSS) benchmark proves the superiority of the proposed method compared to the similar approaches in image steganalysis in terms of detection of stego-image, running time, and diversity measure.