Selective Ensemble Classification of Image Steganalysis Via Deep Q Network

Currently many important advances in digital media field have been achieved through the ensemble method. In image steganalysis, the application of classifiers has evolved from the early single classifier to the ensemble classifiers. The performance of the ensemble classifier is better than that of a single classifier, but the classifiers may have a certain degree of redundancy. Therefore, it is of great significance to study how to reduce the number of the ensemble classifiers under the premise of ensuring the classification performance. In this letter, we propose a selective ensemble method in image steganalysis based on deep Q network, which combines reinforcement learning with convolutional neural network and are seldom seen in ensemble pruning. This method improves the generalization performance of the model, and reduces the size of ensemble as well. The experimental results show that the proposed method has a certain degree of effect on the ensemble classification optimization of image steganalysis in both spatial and frequency domains.

I. Introduction

Image steganography is a technique that is commonly known as covert communication. It hides a secret message in a seemingly harmless image. As an opponent, image steganalysis is a reverse analysis technique that detects steganography. Its purpose is to determine whether the image is embedded by some steganographic algorithms. Generally, steganalysis relies mainly on features and classifiers, so the choice of steganalysis features and classifiers is a core issue.