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AI and ML Approaches in Adaptive Watermarking
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Author(s): Neha Yadav (G.L. Bajaj Institute of Technology and Management, Greater Noida, India), Mayank Singh (G.L. Bajaj Institute of Technology and Management, Greater Noida, India)and Vipin Tyagi (Jaypee University of Engineering and Technology, Guna, India)
Copyright: 2026
Pages: 34
Source title:
Digital Watermarking in Cloud Environments For Copyright Protection
Source Author(s)/Editor(s): Ashwani Kumar (School of Computer Science Engineering & Technology, Bennett University, Greater Noida, UP India), Auzuir Ripardo de Alexandria (Instituto Federal de Educação, Ciência e Tecnologia do Ceará (IFCE), Brazil), Satya Prakash Yadav (Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India)and Antonino Galletta (University of Messina, Italy)
DOI: 10.4018/979-8-3373-3785-2.ch005
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Abstract
Digital watermarking is a crucial method for addressing the threats of data piracy and illegal content distribution in cloud environments and copyright protection systems. This chapter looks at Artificial Intelligence (AI) and Machine Learning (ML) methods of adaptive watermarking, and we will pay close attention to how intelligent systems can make such watermarking solutions more robust, imperceptible, and payload-dense. In addition, this chapter will review the various neural network architectures, deep learning frameworks, reinforcement learning approaches, and hybrid AI system structures that can be utilised in watermarking applications, particularly in cloud computing settings. We will conduct a review of current state-of-the-art AI/ML methods and models for embedding and detecting watermarks. This review will provide a taxonomy of methods, compare these methods, and present case studies and experimental setups.
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