Challenges in complexity and variability of multimedia data have led to revolutions in machine learning techniques. Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. A number of pixels in a digital image collectively conveys certain visual content to viewers. A TV video program consists of both audio and image streams that unfold the underlying story. To recognize the visual content of a digital image, or to understand the underlying story of a video program, we often ignore immediate threat in such visual signals. We may have to Transform Cyber Data into Human-Centered Visualizations and act accordingly to attain needed alertness to warn any danger.
In recent years, some new and powerful modeling and machine learning paradigms have been developed that allow us to glean over massive amounts of data and directly extract useful information for proper decision making, thus creating new techniques to solve those multimedia forensics and security problems with improved performance. 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. Most of these challenges are handled by Machine learning Algorithm and AI for which we call researchers in this area of provide their valuable input to this special issue.
The topics relevant to this Special Issue on Machine learning Based Visual Analytics for Multimedia Security of JINS include, but are not restricted to, the following:
- Visual Analytics for Maritime Video Surveillance
- Visual analytics of multimedia sources, such as texts, audio, speech, and music data; images and videos
- Transforming Cyber Data into Human-Centered Visualizations
- Visual multimedia techniques for surveillance and semantic analysis
- Visualization analytics for social multimedia data
- Machine learning for visualization based active learning
- Multi-metric learning for multi-target visual tracking
- Wireless Security Visual Analytics
- Machine learning for dynamic adaptive video streaming
- Network Defense Visualization
- Machine learning for privacy protection in video surveillance systems
- Machine learning for privacy protection of wireless multimedia data
- Machine learning for wireless multimedia information hiding and fingerprinting
- Machine learning for wireless multimedia data watermarking
Interested authors need to submit their papers according to the following schedule:
Paper submission deadline: 10 August 2019
Final notification: 10 December 2019
Publication date: 10 February 2020 (tentative)
GUEST EDITORS:
Anand Paul, Kyungpook National University, South Korea
anand@knu.ac.kr
Naveen Chilamkurti, La Trobe University, Australia
n.chilamkurti@latrobe.edu.au
Ching-Hsien (Robert) Hsu, National Chung Cheng University, Taiwan
chh@chu.edu.tw