BASN—Learning Steganography with a Binary Attention Mechanism

Pin Wu, Xuting Chang, Yang Yang and Xiaoqiang Li
Additional contact information
Pin Wu: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Xuting Chang: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Yang Yang: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Xiaoqiang Li: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

Future Internet, 2020, vol. 12, issue 3, 1-17

Abstract: Secret information sharing through image carriers has aroused much research attention in recent years with images’ growing domination on the Internet and mobile applications. The technique of embedding secret information in images without being detected is called image steganography. With the booming trend of convolutional neural networks (CNN), neural-network-automated tasks have been embedded more deeply in our daily lives. However, a series of wrong labeling or bad captioning on the embedded images has left a trace of skepticism and finally leads to a self-confession like exposure. To improve the security of image steganography and minimize task result distortion, models must maintain the feature maps generated by task-specific networks being irrelative to any hidden information embedded in the carrier. This paper introduces a binary attention mechanism into image steganography to help alleviate the security issue, and, in the meantime, increase embedding payload capacity. The experimental results show that our method has the advantage of high payload capacity with little feature map distortion and still resist detection by state-of-the-art image steganalysis algorithms.

Keywords: convolutional neural network; steganography; attention mechanism (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/12/3/43/pdf (application/pdf)
https://www.mdpi.com/1999-5903/12/3/43/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:12:y:2020:i:3:p:43-:d:325675

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().