Critical review of bio‐inspired data optimization techniques: An image steganalysis perspective

Abstract

Image steganalysis involves the discovery of secret information embedded in an image. The common method is blind image steganalysis, which is a two‐class classification problem. Blind steganalysis extracts all possible feature variations in an image due to embedding, select the most appropriate feature data, and then classifies the image. The dimensionality of the extracted image features are high and demand data reduction to identify the most relevant features and to aid accurate classification of an image. The classification is under two classes namely, clean (cover) image and stego (image with embedded secret data) image. Since the classification accuracy depends on selection of most appropriate features, opting for the best data reduction or data optimization algorithms becomes a prime requisite. Research shows that most of the statistical optimization techniques converge to local minima and lead to less classification accuracy as compared to bio‐inspired methods. Bio‐inspired optimization methods obtain improved classification accuracy by reducing the high‐dimensional image features. These methods start with an initial population and then optimize them in steps till a global optimal point is reached. Examples of such methods include Ant Lion Optimization (ALO), Fire Fly Algorithm (FFA), and literature shows around 54 such algorithms. Bio‐inspired optimization has been applied in various fields of design optimization and is novel to image steganalysis. This article analyses the various bio‐inspired optimization techniques and their accuracy in image steganalysis pertaining to the discovery of embedded information in both JPEG and spatial domain steganalysis.