Title
An Enhanced Steganography Network for Concealing and Protecting Secret Image Data
Abstract
The development of Internet technology has provided great convenience for data transmission and sharing, but it also brings serious security problems that are related to data protection. As is detailed in this paper, an enhanced steganography network was designed to protect secret image data that contains private or confidential information; this network consists of a concealing network and a revealing network in order to achieve image embedding and recovery separately. To reduce the system's computation complexity, we constructed the network's framework using a down-up structure in order to compress the intermediate feature maps. In order to mitigate the input's information loss caused by a sequence of convolution blocks, the long skip concatenation method was designed to pass the raw information to the top layer, thus synthesizing high-quality hidden images with fine texture details. In addition, we propose a novel strategy called non-activated feature fusion (NAFF), which is designed to provide stronger supervision for synthetizing higher-quality hidden images and recovered images. In order to further boost the hidden image's visual quality and enhance its imperceptibility, an attention mechanism-based enhanced module was designed to reconstruct and enhance the salient target, thus covering up and obscuring the embedded secret content. Furthermore, a hybrid loss function that is composed of pixel domain loss and structure domain loss was designed to boost the hidden image's structural quality and visual security. Our experimental results demonstrate that, due to the elaborate design of the network structure and loss function, our proposed method achieves high levels of imperceptibility and security.
Year
DOI
Venue
2022
10.3390/e24091203
ENTROPY
Keywords
DocType
Volume
data protection, steganography network, computation complexity, non-activated feature fusion, imperceptibility, enhanced module
Journal
24
Issue
ISSN
Citations 
9
1099-4300
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Feng Chen164.07
Qinghua Xing200.34
Bing Sun300.34
Xuehu Yan402.70
Jingwen Cheng500.68