Recent Developments and Applications of Image Watermarking

Dear Colleagues,

The watermarking of multimedia products plays a vital role in copyright protection, authentication, and data security. Over the years, watermarking schemes have proven successful, especially in digital and network environments, often in conjunction with cryptography techniques. However, with the growing trend of printing watermarked images on physical media and capturing them using smartphones, watermarking also faces new challenges due to various attacks such as analog-to-digital transformations, signal and geometric distortions, and camera rotation.

The triple objective of watermarking—robustness, capacity, and imperceptibility—becomes exceptionally challenging in this context. To address these difficulties and explore new possibilities, researchers have been continually advancing traditional signal and pattern recognition techniques. Moreover, the emergence of deep learning technologies has shown great promise for pushing the boundaries of watermarking research.

We are pleased to announce this Topic, which is entitled “Recent Developments and Applications of Image Watermarking”, where we aim to bring together cutting-edge research and applications in this field. This Topic will serve as a platform for researchers and practitioners from various domains, including data hiding, signal processing, and cryptography, to share their original research contributions.

Topics of interest for this topic include, but are not limited to, the following:

• Novel Deep Learning Approaches for Watermarking: Exploring innovative deep learning techniques tailored to watermarking tasks to enhance robustness and imperceptibility.
• Deep Learning for Robust Watermark Detection and Extraction: Investigating methods to reliably detect and extract watermarks from multimedia content despite various attacks.
• Multimodal Watermarking with Deep Learning: Examining approaches that combine deep learning for watermarking in diverse types of media, such as images and audio.
• Deep Learning in Reversible Watermarking: Exploring techniques to achieve reversible watermarking, enabling the perfect recovery of the original content after extraction.
• Data Hiding with Generative Models: Investigating the application of generative models like GANs or VAEs for data hiding and robust watermarking.
• Transfer Learning for Watermarking: Studying the effectiveness of transfer learning in watermarking scenarios, where models trained on one dataset are fine-tuned for other domains.
• Robustness and Attacks: Evaluating the robustness of deep-learning-based watermarking schemes against various attacks, including adversarial and steganalysis attacks.
• Explainability in Watermarking with Deep Learning: Exploring methods to improve the interpretability and explainability of deep learning-based watermarking systems for legal and forensic applications.

We invite researchers and practitioners to submit their original research articles and case studies that shed light on these topics and contribute to the advancement of watermarking techniques.

Dr. Frederic Ros
Dr. Pedro M. B. Torres
Topic Editors