The advent of high-speed internet and modern technologies has led to an exponential rise in the intake and sharing of multimedia content. Popular technologies like big data or fog, cloud, and edge computing also play a key role in providing high flexibility, ubiquity, and scalability to data-related processes. Using these technologies, developers are able to seamlessly extract, store, manage, and share multimedia data across various platforms. Moreover, the combination of cloud-based tools and multimedia has given rise to numerous applications, however, this leap in data consumption suffers from its fair share of security threats, among which data deletion, unauthorized data copying, and third-party data modification are the most common.

These threats thrive on the powerful online multimedia processing services and tools which are freely accessible to anyone on the internet, and so there is an urgent need to secure the copyright of multimedia data and its privacy protections. Currently, multimedia users rely on digital forensics and secret information hiding techniques to safeguard their data from privacy invasion and unauthorized modifications. These techniques complement each other and have successful contributions in a multitude of security applications. However, technologies including edge computing, the Internet of Things (IoT), and big data processing using cloud storage are generating multimedia data in the order of petabytes every day. This creates massive bottlenecks for traditional techniques like digital forensics and information hiding, and so implementing information hiding in real-time and quickly processing huge chunks of data using digital forensics are important challenges. To overcome these challenges, developers have found a workaround by creating graphics processing unit (GPU) processors, which, coupled with large-scale dataset training, have shown great results in the field of computer vision. Moreover, new deep learning techniques are outclassing traditional approaches in terms of maintaining data privacy while sharing multimedia. Although deep learning techniques depend on extensive training processes and have high computational costs, by leveraging robust GPUs, they produce high efficiency results during testing. This allows developers to utilize such techniques to achieve highly accurate computational results in the field of computer vision. Furthermore, these deep learning techniques can support real-time applications that provide information hiding facilities and digital forensic services for multimedia data.

This Special Issue aims to showcase innovative ideas and insightful solutions related to technological enhancement in information hiding and digital forensics for multimedia systems, from both industry and academia. We invite research describing both practical and theoretical assessments related to the design, deployment, and analysis of technologies in the field of data forensics and information hiding in multimedia systems.

Potential topics include but are not limited to the following:

• Secure multimedia data communication for various emerging technologies like big data or fog, cloud, and edge computing using wireless networks
• Multimedia compression technologies for different multimedia wireless networking applications
• Data encoding techniques for secure multimedia data communication
• Secure multimedia data communication for medical applications
• Secure multimedia data communication for biometrics and human-computer interaction
• Secure multimedia data communication for digital games and virtual or augmented reality (VR/AR)
• Cryptographic and steganographic techniques for secure multimedia data communication
• Watermarking techniques for secure multimedia data communication
• Multimedia aggregation techniques for data communication in wireless networks
• Feature extraction techniques for secure and privacy enabled data communication in wireless networks
• Distributed techniques for emerging technologies in multimedia storage technology
• Multimedia tools for emerging technologies for secure multimedia data communication in wireless networks