MODERN METHODS OF DIGITAL IMAGE WATERMARKING: CLASSIFICATION, ANALYSIS AND DEVELOPMENT TRENDS

Abstract

This article presents a comprehensive review of contemporary digital image watermarking methods used to ensure copyright protection, verify authenticity, and trace the sources of digital content distribution.The purpose of the study is to systematize approaches to digital image watermarking, including traditional, hybrid, and deep learning-based methods, and to identify their technical characteristics, strengths and weaknesses, practical efficiency, and scientific and technological challenges in the context of digital security.The methodology is based on a systematic analysis of current scientific sources, including peer-reviewed publications, review articles, experimental studies, and applied implementations of digital watermarking algorithms. The classification of methods was conducted using a set of key technical and functional criteria, including: robustness against attacks, watermark transparency, implementation complexity, computational efficiency, the possibility of blind detection, adaptability to image types, and the breadth of application areas. The comparative analysis focused on assessing the balance between these characteristics, which enables the identification of optimal approaches for practical application in conditions of increasing demands for digital security, privacy, and intellectual property protection.The scientific novelty of the study lies in the integrated generalization of the evolution of watermarking methods, with a particular focus on current technological trends. These include the integration of deep learning to increase the autonomy of algorithms; combining watermarking with cryptographic and blockchain technologies; and the development of adaptive solutions capable of scaling across different image formats and application scenarios.Conclusions. Digital watermarking remains a strategically important tool for protecting digital content. The analyzed methods demonstrate a wide range of solutions with varying degrees of transparency, robustness against attacks, and computational performance. Traditional approaches, based on spatial or frequency domain processing, are well-studied but have limited adaptability. Hybrid methods combine the advantages of classical techniques but require further optimization to withstand sophisticated attacks. Deep learning-based methodologies offer a new level of functionality, including blind watermarking (embedding and extraction without access to the original image), yet face challenges such as high computational costs and the lack of standardized evaluation protocols. In the context of the ongoing digital transformation of society, it is crucial to develop reliable, scalable, and legally regulated watermarking systems capable of functioning effectively under growing requirements for security, privacy, and interoperability across digital platforms.