The rapid growth of digital media exchange and communication technologies has intensified the need for robust methods to ensure data integrity and protect intellectual property. This review paper explores various methodologies for safeguarding digital content, with a particular focus on distributed ledger technologies (DLT) and advanced image processing techniques. DLT, employed in blockchain systems, is highlighted for its ability to secure transactions and data in decentralized networks, mitigating the risks associated with centralized systems. In the realm of multimedia forensics, keypoint-based copy-move forgery detection methods, enhanced with density-based clustering and outlier removal algorithms, have shown superior performance in challenging conditions, effectively identifying forgeries even under geometric distortions and postprocessing. Additionally, the integration of speeded-up robust features (SURF) and polar complex exponential transform (PCET) in forgery detection offers resilience against various distortions, ensuring the authenticity of high-brightness regions in images. The paper also examines the evolution of digital identity management, where blockchain-based systems like BZDIMS employ zero-knowledge proof (ZKP) algorithms to enhance privacy and security. Through a comprehensive comparison of existing models, this paper demonstrates the advantages of these advanced technologies in enhancing data integrity, privacy, and the overall reliability of digital content verification systems.