Privacy-Preserving of Multimedia Processing for Data Learning

Dear Colleagues,

In recent years, with the rapid development of artificial intelligence and high-performance computing, the data learning application demand for large-scale multimedia data has become increasingly extensive. Data learning plays an important role in deep learning, edge computing, and service outsourcing, to help models and architectures quickly improve data interaction capabilities. Data learning relies on state-of-the-art machine learning algorithms to learn data and use those learned models to automate and accelerate the tedious aspects of the interactions between experts and data, enabling humans and computers to each focus on what they are good at, respectively.

In data learning, privacy preserving of multimedia processing is an important aspect to be considered. How to improve the ability of data learning while ensuring the security of multimedia data is the focus of data learning research.

This Special Issue plans to cover a wide range of topics, including data security, privacy preserving, and the application of data learning in the future.

Both review articles and original research papers pertaining to data learning and privacy preserving are welcome. The topics of interest include, but are not limited to, the following:

• Data Learning;
• Big Data Security;
• AI Security;
• Data Hiding;
• Multimedia Data Processing;
• Video/Image Watermarking;
• Differential Privacy;
• Multimedia Data Coding Technique;
• Active/Passive Forensics;
• Deep Learning Model Watermarking;
• Cloud Data Security.

Prof. Dr. Zhangjie Fu
Prof. Dr. Lizhi Xiong
Guest Editors

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