Authors - Vishakha Shinde, Himangi Pande Abstract - The rapid growth of digital communication platforms, cloud multimedia sharing and AI-driven visual systems has increased the demand for secure image ownership verification and multimedia authentication. Conventional watermarking approaches often suffer from limited robustness against compression, geometric distortion and adversarial attacks. Recent advances in deep learning and cryptographic protection mechanisms have enabled the development of intelligent hybrid watermarking frameworks with improved robustness, adaptive embedding and secure ownership verification. This paper presents a comprehensive review of secure image watermarking techniques integrating deep learning architectures and cryptographic security schemes. The study analyzes CNN-, autoencoder-, GAN- and diffusion-based watermarking frameworks along with encryption-assisted watermark embedding strategies, attack resilience mechanisms and lightweight watermarking systems for edge– IoT environments. Benchmark datasets, performance metrics, loss functions and quantitative comparisons of existing frameworks are also discussed. The comparative analysis indicates that hybrid deep learning–cryptographic watermarking methods significantly improve robustness, authentication reliability and resistance against signal-processing, geometric and adversarial attacks. However, computational complexity, scalability and real-time deployment constraints remain major challenges. Finally, the paper discusses future research
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