Author:
Zhang Han,Song Zhihua,Xing Qinghua,Feng Boyu,Lin Xiangyang
Abstract
In recent years, several steganalysis neural networks have been proposed and achieved satisfactory performances. However, these deep learning methods all encounter the problem of Training-Images-Shortage (TIS). In most cases, it is difficult for steganalyses to obtain enough signals about steganography from a game opponent. In order to solve the problem of TIS for steganalysis, we propose a novel steganalysis method based on generative learning and deep residual convolutional neural networks. Comparative experiments show that the proposed architecture can achieve promising performance in response to spatial domain steganalysis despite a lack of training images.
Funder
National Natural Science Foundation of China
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering