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IETE Technical Review

Call for Papers | Artificial Intelligence Oriented Information Hiding and Multimedia Forensics

Aims and Scope

 

With the rapid growth and use of multimedia signal processing and Internet technology, a number of security issues have also emerged correspondingly in recent years, such as copyright identification, covert communication using multimedia files, copy-move forgery in digital images and videos, and biometric spoofing. Meanwhile, artificial intelligence has been widely studied on solving a variety of difficult problems including multimedia security, such as convolution neural networks for steganalysis and forensics, generative adversarial networks for coverless steganography, deep learning for signal and image processing in encrypted domain, and privacy preserving. Nevertheless, there are some notable shortcomings on performances. Current studies for multimedia content security are usually based on strict conditions that are nearly impossible to meet up in real world. Furthermore, high computation complexity of current methods makes it hard to handle big data in cloud computing environment. Therefore, the topic of intelligent multimedia hiding and forensics deserves in-depth investigation.

This special issue for intelligent multimedia hiding and forensics focuses on the new methods of information hiding and forensics for multimedia data with artificial intelligent techniques, including watermarking, steganography, multimedia hashing, forensics, copyright protection, and privacy preserving, which have been a comparatively hot topic with great value in multimedia security community. The research on intelligent multimedia hiding and forensics is of great significance for fighting against illegal and criminal activities on Internet.

Topics of interest

 

Possible topics for the manuscripts submitted to this special issue include, but are not limited to:

  • Intelligent steganography for multimedia using convolutional neural networks
  • Intelligent steganalysis for multimedia based on deep learning
  • Coverless data hiding for multimedia using generative model
  • Multimedia hashing based on deep learning
  • Multimedia forensics based on deep learning
  • Multimedia anti-forensics using adversarial networks
  • Robust, fragile and semi-fragile watermarking for multimedia
  • Reversible data hiding for multimedia
  • Intelligent multimedia signal processing in encrypted domain
  • Tampering detection in multiple operator chains
  • Visual cryptography and secret image sharing
  • Multimedia fingerprinting and traitor tracing
  • Multimedia privacy-preserving techniques
  • Multimedia security on network protocol
  • Intelligent analysis for covert communication and surveillance

Instructions for Manuscripts

 

  1. All manuscripts should follow the author instructions of IETE Technical Review at www.tandfonline.com/titr and be submitted online at ScholarOne ManuscriptsTM submission site: https://mc.manuscriptcentral.com/titr.
  2. During the submission, please indicate the manuscripts are submitted to the special issue of Artificial Intelligence Oriented Information Hiding and Multimedia Forensics by choosing the corresponding article type.
  3. The selected conference papers must undergone substantial extensions of at least 40% new contents with respect to the original versions before submission.
  4. All accepted articles in this special issue will have Green Open Access, and details can be found: https://authorservices.taylorandfrancis.com/publishing-open-access/#Green%20OA.
Important Dates 
Submission of papers:31 January, 2020
Notification of review results:30 April, 2020
Submission of revised papers:30 June, 2020
Notification of final decision:31 August, 2020

Guest Editors

Prof. Chuan Qin (qin@usst.edu.cn), 
University of Shanghai for Science and Technology, China

Prof. Xiaolong Li (lixl@bjtu.edu.cn), 
Beijing Jiaotong University, China

Prof. Zhenxing Qian (zxqian@fudan.edu.cn)
Fudan University, China

Prof. Jinwei Wang (wjwei@nuist.edu.cn), 
Nanjing University of Information Science & Technology, China