Invisible and Resilient Digital Watermarking of Multimedia Using AI

Abstract

The digital multimedia knows an exponential growth which leads to major problems associated to privacy, authenticity, data protection, copyright protection, and data integrity. A brief survey has shown that traditional digital watermarking methods such as LSB, DCT, DWT, and SVD do not tolerate compression, geometric distortions, noise and hybrid/combined attacks. Furthermore, these conventional techniques lack of adaptability to various media formats. Recently, with the exponential developments in the fields artificial intelligence (AI) and Deep Learning, it has been proven to strengthen and help the creation of robust and invisible digital watermarking systems that jointly optimize imperceptibility and resilience. At this regard, this article proposes a novel simplified AIbased digital watermarking approach that exceeds the performance of the commonly used approaches such as HiDDeN and InvisMark by taking into account their computational complexity, reproducibility, and deployment in complex and constrained environments. The proposed method in this study is focused on the use of an encoder-decoder pipline merged with latent space insertion, adversarial learning, and resilient perturbation simulation. These components involvement was considered in the design of the solution to achieve an optimal balance between robustness, efficiency and invisibility. In contrast, available approaches focuse merely and basically on processing images, while this study also extend digital watermaking to videos by implementing 3D CNNs and ConvLSTMs. Improvements over the original study, these include conserving the core concept of the idea behind the study: temporal consistency and stability due to distortion and codec reencoding. Other issues including security and regulatory considerations are discussed in this work with respect to responsible use in real-world applications. compliance with data protection frameworks. Further, The concerns should be checked and verified with regard to resilience and critical ethical against removal attacks using AI Advanced techniques. Hence, by considering all these aspects, this work presents a demonstrate the potential of AI-based watermarking to provide scalable, invisible, and robust protection for digital media both in academics, science, and industries.