Feature-guided Deep Subdomain Adaptation Network for Dataset Mismatch in Spatial Steganalysis

作     者：Zhang, Lei Wang, Hongxia He, Peisong Abdullahi, Sani M. Li, Bin 

作者机构：School of Cyber Science and Engineering Sichuan University Chengdu610065 China College of Computer and Information Technology China Three Gorges University Yichang443002 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China 

出 版 物：《Research Square》 (Research Square)

年 卷 期：2021年

核心收录：

主　　题：Steganography 

摘      要：Steganalysis aims to detect covert communication established via steganography. In recent years, numerous deep learning-based image steganalysis methods with high performance have been proposed. However, these methods tend to suffer from distinct performance degradation when cover images in the train and test set are quite different, also known as cover source mismatch. To address this limitation, in this paper, a feature-guided deep subdomain adaptation network is proposed. Initially, the predictions of the pretrained model are used as pseudo labels to divide the unlabeled samples of the target domain into different subdomains, and the distributions of the relevant subdomains are aligned by subdomain adaptation. Afterwards, since the steganalysis model may assign incorrect predictions to samples in the target domain, we integrate guiding features to make the division of subdomains more precise. The experimental results show that the proposed network is significantly better than other three networks such as Steganalysis Residual Network (SRNet), deep adaptive network (J-Net) and Deep Subdomain Adaptation Network (DSAN), when it is used to detect three spatial steganographic algorithms with a wide variety of datasets and payloads. Especially, compared with SRNet, the average accuracy of our method is increased by 5.4% at 0.4bpp and 8.5% at 0.2bpp in the case of dataset mismatch. © 2021, CC BY.