Blind Image Steganalytic Optimization by using Machine Learning

Abstract: Since antiquity, steganography has existed in protecting sensitive information against unauthorized unveiling attempts. Nevertheless, digital media’s evolution, reveals that steganography has been used as a tool for activities such as terrorism or child pornography. Given this background, steganalysis arises as an antidote to steganography. Steganalysis can be divided into two main approaches: universal – also called blind – and specific. Specific methods request a previous knowledge of the steganographic technique under analysis. On the other hand, universal methods which can be widely practiced in a variety of algorithms, are more adaptable to real-world applications. Thus, it is necessary to establish even more accurate steganalysis techniques capable of detecting the hidden information coming from the use of diverse steganographic methods. Considering this, a universal steganalysis method specialized in images is proposed. The method is based on the typical steganalysis process, where feature extractors and classifiers are used. The experiments were implemented on different embedding rates and for various steganographic techniques. It turns out that the proposed method succeeds for the most part, providing dignified results on color images and promising results on gray-scale images.