Table of Contents Author Guidelines Submit a Manuscript

Machine Learning for Wireless Multimedia Data Security

Call for Papers

With the rapid development of multimedia technologies, the collection and modification of wireless multimedia data have become greatly convenient and easy. Meanwhile, the wireless multimedia data also made sensitive information available to potential attackers. The credibility of digital wireless multimedia data has thus decreased if the wireless multimedia data cannot be well protected. In addition, the copyright and privacy of wireless multimedia data also are easy to be infringed. The data storage and computation, especially, have to be delegated to the powerful but always untrusted cloud, which has led to a series of challenging security and privacy threats.

Nowadays, artificial intelligence (AI) technology has been widely used in academia and industry. Machine learning can be regarded as one of the most important AI technologies, and it has been successfully used in image processing, pattern recognition, computer vision, natural language processing, and so on. Currently, the traditional steganography and security of encrypted wireless multimedia data face a lot of challenges; thus, new types of steganography and encryption of wireless multimedia data, including audio, image, and video, need to be explored urgently. Moreover, in a new environment like cloud computing, the distribution and processing of wireless multimedia data also face more new challenges. For example, how to securely process wireless multimedia data in cloud computing to preserve the privacy of wireless multimedia data, and how to reliably solve a multiparty computation by outsourcing are still open questions.

This special issue is intended for researchers and practitioners from academia as well as industry who are interested in issues that arise from using machine learning for wireless multimedia data security.

Potential topics include but are not limited to the following:

  • Machine learning for wireless multimedia data security
  • Machine learning for wireless multimedia data transmission security
  • Machine learning for reversible data hiding in plaintext or ciphertext multimedia
  • Machine learning for reliable computing and outsourcing of wireless multimedia data
  • Machine learning for threat detection of wireless multimedia data system
  • Machine learning for privacy protection of wireless multimedia data
  • Machine learning for wireless multimedia information hiding and fingerprinting
  • Machine learning for wireless multimedia authentication and encryption
  • Machine learning for wireless multimedia data copyright protection
  • Machine learning for wireless multimedia data watermarking

Authors can submit their manuscripts through the Manuscript Tracking System at https://mts.hindawi.com/submit/journals/scn/mlmds/.

Submission DeadlineFriday, 31 August 2018
Publication DateJanuary 2019

Papers are published upon acceptance, regardless of the Special Issue publication date.

Lead Guest Editor

  • Zhaoqing Pan, Nanjing University of Information Science and Technology, Nanjing, China

Guest Editors