Table of Content

Advances in Watermarking Techniques using Artificial Intelligence in Multimedia and Communication

Submission Deadline: 01 February 2023 (closed) Submit to Special Issue

Guest Editors

Dr. Uzair Aslam Bhatti, Hainan University, China.
Prof. Mehedi Masud, Taif University, Saudi Arabia.
Dr. Sibghat Ullah Bazai, Balochistan University of Information Technology, Engineering, and Management Sciences (BUITEMS), Pakistan.
Prof. Li JingBing, Hainan University, China.

Summary

Watermarks are an effective way to protect copyrights and are widely used in massive Internet images and multimedia content. The importance of various approaches to watermarks, such as the detection of watermarks and the removal of watermarks removal, is expanding. Signal processing has been the primary focus of research into watermarking technology for two-dimensional photographs up until fairly recently. During the processing that is necessary for storing or distributing content, watermarked content and information are vulnerable to both malicious attacks and non-malicious attacks. Malicious attacks are designed to damage or remove watermark information, while non-malicious attacks are designed to damage or remove watermark information. However, watermark extraction is almost never possible to accomplish algorithmically or deterministically. Watermark embedding, on the other hand, can be done anyway. Because the signal or information that has been attacked was not a signal that was predicted at the time that the algorithmic extraction method was developed, it is possible that it will not be possible to guarantee that the watermark that was inserted by the algorithmic extraction method can be extracted. Recent research has led to the development of a system that uses neural networks to do watermarking, which is one of the proposed solutions for overcoming this constraint.
A user can easily process multimedia content using various deep neural networks, such as CNN and GAN, thanks to the rapid development of artificial intelligence (AI) technology in recent years. As a result, this poses a serious challenge to the protection of personal privacy and information security. Research into the AI-based protection and safety of audiovisual content has been receiving a lot of attention as a direct result. Concerns pertaining to the preservation and detection of multimedia content are always best understood in terms of an asymmetrical signal processing model. To achieve covert communication, copyright protection, and authentication, secret data are embedded into multimedia files using a series of signal processing methods. The corresponding detection technique is an inverse technology, and its primary purpose is to discover the presence of secret data or the presence of illegal tampering behavior. In the most recent years, this topic has given rise to a number of remarkable works. Despite this, a number of challenges remain in the relevant sectors, particularly in social multimedia forensics, steganography and steganography techniques, digital watermarking, network privacy protection, artificial intelligence security, and other related areas.

• multimedia security techniques using AI
• digital watermarking using AI 
• neural networks applications in multimedia security
• deep neural network design for digital watermarking
• attack modeling for machine learning-based watermarking
• machine learning-based content (information) security
• machine learning-based stenography technology
• network security using AI
• hardware or software implementation (development) of machine learning-based watermarking


Keywords

Machine learning, Multimedia Security, Watermarking, Information protection

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