投稿地址： https://jei.msubmit.net/cgi-bin/main.plex

Guest Editors

Shi Dong

Zhoukou Normal University
Zhoukou, China
dongshi@zknu.edu.cn

Stelvio Cimato

Università degli studi di Milano
Italy
stelvio.cimato@unimi.it

Joarder Kamruzzaman

Federation University Australia
Ballarat, Australia
joarder.kamruzzaman@federation.edu.au

Yongsheng Hao

Nanjing University of Information Science & Technology
Nanjing, China
002004@nuist.edu.cn

Scope

As an important branch of network security, image steganography has set off a research upsurge since it was proposed and plays an important role in all kinds of copyright protection. With social changes, the traditional way of hiding important information in the carrier at the cost of permanent distortion of the carrier can no longer meet more practical needs. For example, in medicine it is necessary to ensure not only the security of information in the transmission process but also the lossless recovery of the carrier after the receiver extracts the hidden information. In the military, the security of carrier information is much more important than the amount of information that can be embedded. With the introduction of a large amount of data, the way of storing information in the cloud is also facing the challenges of data security and reversible information extraction. In addition, massive spreading of harmful information will increase the difficulty of security management. Image steganalysis techniques can address such concerns. Of particular interest here is real-time image steganalysis when information timeline is of importance. How to use new technologies such as deep learning and pattern recognition to improve the embedding rate and real-time steganalysis and how to reduce time complexity are ongoing research problems.

This special section is intended to serve as a forum to provide recent advances in real-time image steganography and steganalysis technology as related to the following: (1) state-of-the-art real-time steganalysis models based on deep learning; (2) novel real-time image hiding frameworks; (3) real-time reversible image hiding technology in encrypted domain; (4) real-time steganography and steganalysis using computational intelligence; (5) real-time multi-media security and watermarking; and (6) survey articles reporting the recent progress in real-time image hiding and steganalysis. 

Topics of interest include but are not limited to:

• Real-time image steganalysis model based on deep learning

• Real-time reversible image steganography technology

• Real-time reversible image steganography technology in encrypted domain

• Real-time reversible image steganography in shared domain

• Securing social media real-time image steganography

• Blockchain based real-time image steganography

• Deep learning based real-time image steganography

• Real-time or online image steganography

• Real-time combination of encryption, watermarking for privacy protection and authentication

• Improved real-time methods for traditional image steganography technology

• Real-time semi learning steganalysis model

• Adversarial examples of real-time detection based on image steganalysis

• Comprehensive survey articles on recent real-time image steganography and steganalysis techniques highlighting future challenges

Manuscripts should conform to the author guidelines of the Journal of Electronic Imaging. Prospective authors should submit their manuscript through the online submission system at https://jei.msubmit.net. The special section should be mentioned in the cover letter. Each manuscript will be reviewed by at least two independent reviewers. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within six weeks. Each paper is published as soon as the copyedited and typeset proofs are approved by the author.

As an important branch of network security, image steganography has set off a research upsurge since it was proposed and plays an important role in all kinds of copyright protection. With social changes, the traditional way of hiding important information in the carrier at the cost of permanent distortion of the carrier can no longer meet more practical needs. For example, in medicine it is necessary to ensure not only the security of information in the transmission process but also the lossless recovery of the carrier after the receiver extracts the hidden information. In the military, the security of carrier information is much more important than the amount of information that can be embedded. With the introduction of a large amount of data, the way of storing information in the cloud is also facing the challenges of data security and reversible information extraction. In addition, massive spreading of harmful information will increase the difficulty of security management. Image steganalysis techniques can address such concerns. Of particular interest here is real-time image steganalysis when information timeline is of importance. How to use new technologies such as deep learning and pattern recognition to improve the embedding rate and real-time steganalysis and how to reduce time complexity are ongoing research problems.

This special section is intended to serve as a forum to provide recent advances in real-time image steganography and steganalysis technology as related to the following: (1) state-of-the-art real-time steganalysis models based on deep learning; (2) novel real-time image hiding frameworks; (3) real-time reversible image hiding technology in encrypted domain; (4) real-time steganography and steganalysis using computational intelligence; (5) real-time multi-media security and watermarking; and (6) survey articles reporting the recent progress in real-time image hiding and steganalysis. 

Topics of interest include but are not limited to:

• Real-time image steganalysis model based on deep learning

• Real-time reversible image steganography technology

• Real-time reversible image steganography technology in encrypted domain

• Real-time reversible image steganography in shared domain

• Securing social media real-time image steganography

• Blockchain based real-time image steganography

• Deep learning based real-time image steganography

• Real-time or online image steganography

• Real-time combination of encryption, watermarking for privacy protection and authentication

• Improved real-time methods for traditional image steganography technology

• Real-time semi learning steganalysis model

• Adversarial examples of real-time detection based on image steganalysis

• Comprehensive survey articles on recent real-time image steganography and steganalysis techniques highlighting future challenges

Manuscripts should conform to the author guidelines of the Journal of Electronic Imaging. Prospective authors should submit their manuscript through the online submission system at https://jei.msubmit.net. The special section should be mentioned in the cover letter. Each manuscript will be reviewed by at least two independent reviewers. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within six weeks. Each paper is published as soon as the copyedited and typeset proofs are approved by the author.