A Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism

Image steganalysis is a technique for detecting the presence of hidden information in images, which has profound significance for maintaining cyberspace security. In recent years, various deep steganalysis networks have been proposed in academia, and have achieved good detection performance. Althoug...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yuwei Ge, Tao Zhang, Haihua Liang, Qingfeng Jiang, Dan Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	image steganalysis deep learning convolutional neural networks adversarial training Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/9ad76ca8524045a3985d8e97d1a9e6ae
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:9ad76ca8524045a3985d8e97d1a9e6ae
record_format	dspace
spelling	oai:doaj.org-article:9ad76ca8524045a3985d8e97d1a9e6ae2021-11-25T17:24:18ZA Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism10.3390/electronics102227422079-9292https://doaj.org/article/9ad76ca8524045a3985d8e97d1a9e6ae2021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2742https://doaj.org/toc/2079-9292Image steganalysis is a technique for detecting the presence of hidden information in images, which has profound significance for maintaining cyberspace security. In recent years, various deep steganalysis networks have been proposed in academia, and have achieved good detection performance. Although convolutional neural networks (CNNs) can effectively extract the features describing the image content, the difficulty lies in extracting the subtle features that describe the existence of hidden information. Considering this concern, this paper introduces separable convolution and adversarial mechanism, and proposes a new network structure that effectively solves the problem. The separable convolution maximizes the residual information by utilizing its channel correlation. The adversarial mechanism makes the generator extract more content features to mislead the discriminator, thus separating more steganographic features. We conducted experiments on BOSSBase1.01 and BOWS2 to detect various adaptive steganography algorithms. The experimental results demonstrate that our method extracts the steganographic features effectively. The separable convolution increases the signal-to-noise ratio, maximizes the channel correlation of residuals, and improves efficiency. The adversarial mechanism can separate more steganographic features, effectively improving the performance. Compared with the traditional steganalysis methods based on deep learning, our method shows obvious improvements in both detection performance and training efficiency.Yuwei GeTao ZhangHaihua LiangQingfeng JiangDan WangMDPI AGarticleimage steganalysisdeep learningconvolutional neural networksadversarial trainingElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2742, p 2742 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	image steganalysis deep learning convolutional neural networks adversarial training Electronics TK7800-8360
spellingShingle	image steganalysis deep learning convolutional neural networks adversarial training Electronics TK7800-8360 Yuwei Ge Tao Zhang Haihua Liang Qingfeng Jiang Dan Wang A Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism
description	Image steganalysis is a technique for detecting the presence of hidden information in images, which has profound significance for maintaining cyberspace security. In recent years, various deep steganalysis networks have been proposed in academia, and have achieved good detection performance. Although convolutional neural networks (CNNs) can effectively extract the features describing the image content, the difficulty lies in extracting the subtle features that describe the existence of hidden information. Considering this concern, this paper introduces separable convolution and adversarial mechanism, and proposes a new network structure that effectively solves the problem. The separable convolution maximizes the residual information by utilizing its channel correlation. The adversarial mechanism makes the generator extract more content features to mislead the discriminator, thus separating more steganographic features. We conducted experiments on BOSSBase1.01 and BOWS2 to detect various adaptive steganography algorithms. The experimental results demonstrate that our method extracts the steganographic features effectively. The separable convolution increases the signal-to-noise ratio, maximizes the channel correlation of residuals, and improves efficiency. The adversarial mechanism can separate more steganographic features, effectively improving the performance. Compared with the traditional steganalysis methods based on deep learning, our method shows obvious improvements in both detection performance and training efficiency.
format	article
author	Yuwei Ge Tao Zhang Haihua Liang Qingfeng Jiang Dan Wang
author_facet	Yuwei Ge Tao Zhang Haihua Liang Qingfeng Jiang Dan Wang
author_sort	Yuwei Ge
title	A Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism
title_short	A Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism
title_full	A Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism
title_fullStr	A Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism
title_full_unstemmed	A Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism
title_sort	novel technique for image steganalysis based on separable convolution and adversarial mechanism
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9ad76ca8524045a3985d8e97d1a9e6ae
work_keys_str_mv	AT yuweige anoveltechniqueforimagesteganalysisbasedonseparableconvolutionandadversarialmechanism AT taozhang anoveltechniqueforimagesteganalysisbasedonseparableconvolutionandadversarialmechanism AT haihualiang anoveltechniqueforimagesteganalysisbasedonseparableconvolutionandadversarialmechanism AT qingfengjiang anoveltechniqueforimagesteganalysisbasedonseparableconvolutionandadversarialmechanism AT danwang anoveltechniqueforimagesteganalysisbasedonseparableconvolutionandadversarialmechanism AT yuweige noveltechniqueforimagesteganalysisbasedonseparableconvolutionandadversarialmechanism AT taozhang noveltechniqueforimagesteganalysisbasedonseparableconvolutionandadversarialmechanism AT haihualiang noveltechniqueforimagesteganalysisbasedonseparableconvolutionandadversarialmechanism AT qingfengjiang noveltechniqueforimagesteganalysisbasedonseparableconvolutionandadversarialmechanism AT danwang noveltechniqueforimagesteganalysisbasedonseparableconvolutionandadversarialmechanism
_version_	1718412423268925440

A Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism

Ejemplares similares