Deep Learning for Image Watermarking: A Comprehensive Review and Analysis of Techniques, Challenges, and Applications

Abstract

Highlights: What are the main findings? Deep learning-based watermarking methods (CNN, GAN, Transformers, and diffusion models) significantly outperform traditional spatial- and frequency-domain techniques in terms of robustness, transparency, and adaptability to modern attack types. Emerging architectures such as Vision Transformers, Swin Transformers, and diffusion models introduce new capabilities, notably higher resistance to generative and latent-space attacks, as well as increased watermark capacity. What are the implications of the main findings? The rapid evolution of neural network architectures accelerates the development of watermarking systems capable of protecting digital content against increasingly sophisticated threats, including AI-generated manipulations. Future watermarking deployments will require optimized, scalable, and computationally efficient deep learning architectures to support real-time applications in cybersecurity, multimedia distribution, IoT systems, and content authenticity verification. The growing demand for digital content protection has significantly increased the importance of image watermarking, particularly in light of the rising vulnerability of multimedia content to unauthorized modifications. In recent years, research has increasingly focused on leveraging deep learning architectures to enhance watermarking performance, addressing challenges related to transparency, robustness, and payload capacity. Numerous deep learning-based watermarking methods have demonstrated superior effectiveness compared to traditional approaches, particularly those based on Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Transformers, and diffusion models. This paper presents a comprehensive survey of recent developments in both conventional and deep learning-based image watermarking techniques. While traditional methods remain prevalent, deep learning approaches offer notable improvements in embedding and extraction efficiency, particularly when facing complex attacks, including those generated by advanced AI models. Applications in areas such as deepfake detection, cybersecurity, and Internet of Things (IoT) systems highlight the practical significance of these advancements. Despite substantial progress, challenges remain in achieving an optimal balance between invisibility, robustness, and capacity, particularly in high-resolution and real-time scenarios. This study concludes by outlining future research directions toward develop robust, scalable, and efficient deep learning-based watermarking systems capable of addressing emerging threats in digital media environments.