Digital watermarking challenges are multiple. The most important are copyright protection, integrity control, and indexing. The latter has gained significant attention from the research community due to the increasing production of multimedia data. Existing anaglyph 3D video watermarking methods are developed mainly for copyright protection applications but they have not targeted indexing applications that facilitate the retrieving of anaglyph 3D videos in databases. Hence, it is necessary to develop a robust watermarking technique dedicated to indexing in order to both protect and facilitate retrieving anaglyph 3D videos. This paper presents a novel watermarking-based retrieval system for anaglyph 3D videos. The system utilizes a mosaic image generated from the original video as the signature, providing a comprehensive representation of the video’s content. To handle the large size of the mosaic image, it is divided into a set of vectors. The obtained vectors are embedded into different selected frames using a dynamic insertion key to ensure visual quality and robustness against collusion attacks. During retrieval, the extracted signatures from the database are compared with the query video’s mosaic image using a similarity measure. Experimental results demonstrate high visual quality, robustness, and effectiveness of the proposed system.

In recent years, the advent of the Internet and the rapid growth of digital media applications have made video a primary medium for information transmission. However, this progress has also brought about new challenges, including concerns related to unauthorized copying, digital plagiarism, and the distribution and utilization of copyrighted digital content. In order to address these issues, watermarking has emerged as a solution. It involves embedding a watermark into a digital cover and subsequently extracting it to resolve ownership disputes and copyright infringements related to media content. Numerous conventional video watermarking techniques have been introduced, demonstrating their effectiveness in achieving both invisibility and robustness against various types of attacks. Recently, the application of deep learning principles in embedding signatures into video content has gained significant attention. This approach offers considerable advantages in the field of watermarking due to its accuracy, superior outcomes, and exceptional learning capabilities. This paper provides an overview of recent advancements in deep learning-based video watermarking. It categorizes the proposed approaches according to the employed network architecture, offering a comprehensive summary of the field’s latest developments. The study concludes by examining potential research avenues in the domain of deep learning-based video watermarking.

In this paper, a robust 3D triangular mesh watermarking algorithm based on 3D segmentation is proposed. In this algorithm three classes of watermarking are combined. First, we segment the original image to many different regions. Then we mark every type of region with the corresponding algorithm based on their curvature value. The experiments show that our watermarking is robust against numerous attacks including RST transformations, smoothing, additive random noise, cropping, simplification and remeshing.