The paper "Deep Learning-Based Watermarking Techniques Challenges: A Review of Current and Future Trends" by Saoussen Ben Jabra and Mohamed Ben Farah provides an in-depth review of the advancements and challenges in deep learning-based watermarking techniques. The authors highlight the increasing vulnerability of multimedia content to unauthorized alterations, leading to a growing interest in deep learning for watermarking. They discuss the efficiency and robustness of deep learning-based methods compared to traditional techniques, particularly in image watermarking. However, they note that deep learning models for video watermarking are still in their early stages, with limited research compared to image watermarking. The paper is structured into several sections, including an introduction, a review of traditional image and video watermarking techniques, an overview of deep learning-based watermarking concepts, a detailed review of deep learning-based image watermarking, and a review of deep learning-based video watermarking. Each section covers the key methodologies, architectures, and datasets used in the field. Key points discussed include: - The importance of watermarking in protecting digital content from unauthorized alterations. - The limitations of traditional watermarking techniques, such as low robustness against attacks and poor invisibility. - The advantages of deep learning-based watermarking, including reusability, adaptability to different attacks, and improved security. - The classification of deep learning-based watermarking techniques based on network architecture (e.g., CNNs and GANs) and embedding domain (e.g., spatial, frequency, and hybrid domains). - The performance and challenges of deep learning-based image and video watermarking techniques, with a focus on robustness, invisibility, and capacity. The authors conclude by discussing future research directions, emphasizing the need for more robust and efficient deep learning-based video watermarking techniques to address the unique challenges of video content, such as temporal redundancy and compression. They also highlight the importance of combining different deep learning models and techniques to enhance the overall performance of watermarking systems.The paper "Deep Learning-Based Watermarking Techniques Challenges: A Review of Current and Future Trends" by Saoussen Ben Jabra and Mohamed Ben Farah provides an in-depth review of the advancements and challenges in deep learning-based watermarking techniques. The authors highlight the increasing vulnerability of multimedia content to unauthorized alterations, leading to a growing interest in deep learning for watermarking. They discuss the efficiency and robustness of deep learning-based methods compared to traditional techniques, particularly in image watermarking. However, they note that deep learning models for video watermarking are still in their early stages, with limited research compared to image watermarking. The paper is structured into several sections, including an introduction, a review of traditional image and video watermarking techniques, an overview of deep learning-based watermarking concepts, a detailed review of deep learning-based image watermarking, and a review of deep learning-based video watermarking. Each section covers the key methodologies, architectures, and datasets used in the field. Key points discussed include: - The importance of watermarking in protecting digital content from unauthorized alterations. - The limitations of traditional watermarking techniques, such as low robustness against attacks and poor invisibility. - The advantages of deep learning-based watermarking, including reusability, adaptability to different attacks, and improved security. - The classification of deep learning-based watermarking techniques based on network architecture (e.g., CNNs and GANs) and embedding domain (e.g., spatial, frequency, and hybrid domains). - The performance and challenges of deep learning-based image and video watermarking techniques, with a focus on robustness, invisibility, and capacity. The authors conclude by discussing future research directions, emphasizing the need for more robust and efficient deep learning-based video watermarking techniques to address the unique challenges of video content, such as temporal redundancy and compression. They also highlight the importance of combining different deep learning models and techniques to enhance the overall performance of watermarking systems.