Watermarking Techniques for Content Integrity Verification, Tamper Detection and Forensics in Synthetic Media

Digital watermarking has evolved from its roots in copyright protection to a vital technology for ensuring integrity, authenticity, and traceability across critical applications. This article examines emerging trends in watermarking techniques tailored for integrity verification, tamper detection, and forensics, driven by challenges posed by AI-generated media, IoT ecosystems, and adversarial threats. Traditional methods, often limited by manual feature engineering, are being supplanted by deep neural networks (DNNs) that optimize imperceptibility, robustness, and capacity through datadriven, end-to-end learning. Innovations include attention mechanisms for adaptive embedding and adversarial training to counter GAN-based attacks, achieving detection accuracy exceeding 90 % under hostile conditions. Critical domains such as synthetic media relies on proactive watermarking to distinguish AI-generated content and combat deepfakes. The review underscores the interplay between evolving technical requirements-balancing imperceptibility, robustness, security-and domain-specific needs, from healthcare to smart infrastructure.