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With rapid development of network technologies and the wide use of digital camera devices, digital multimedia (in particular image and video data) has grown tremendously on the internet. However, by using various powerful multimedia processing tools, the multimedia data is getting easier to be illegally copied, and forged. Consequently, the copyright and privacy protection of multimedia data have been an urgent requirement in the field of multimedia security. To prevent multimedia data from unauthorized use and privacy invasion, two typical approaches have been proposed: information hiding and digital forensics. Both of these approaches have been applied successfully in many digital security applications and they can also complement each other nicely. However, in cloud computing and big data environments, there exist massive amount of multimedia data and new multimedia data keeps growing exponentially. In this context, the information hiding and digital forensics face many new challenges. For example, how to conduct information hiding and digital forensics in real-time, and how to rapidly process a large amount of multimedia data? Nowadays, with the development of graphic processing unit (GPU) processors and the availability of large-scale training datasets such as ImageNet, the deep learning techniques have gained success in the field of machine learning and computer vision, and have shown to outperform many traditional techniques. Although deep learning techniques require computationally extensive off-line training process, with the help of powerful GPUs, they show high efficiency at online testing stage, which enables achieving real-time computation performance for a variety of computer vision tasks. Also, it is possible to explore the deep learning techniques for various real-time multimedia security applications, such as real-time information hiding and digital forensics in order to achieve desirable performances in the terms of both accuracy and efficiency. The aim of this special issue is to publish the latest research works in the related areas of real-time information hiding and forensics. It provides the effective and efficient deep learning techniques or other intelligent techniques that have potential to address challenges of real-time information hiding and digital forensics. According to a rigorous review procedure, each manuscript submitted to the special issue was reviewed by two or more anonymous experts. Based on the reviews’ comments, a total of 16 papers have been selected to be included in this special issue, which fall into the following three major categories: (1) image forensics (9 papers), (2) steganography and steganalysis (4 papers), and (3) other emerging security topics (3 papers). In the following sections, an overview of these papers is provided. * Zhili Zhou zhou_zhili@163.com

Introduction to the special issue on deep learning for real-time information hiding and forensics