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<p>As there has been an exponential growth of video watching on digital media, protecting multimedia content against piracy, forgery, and unauthorized sharing has become an essential challenge. Conventional cryptographic techniques secure data only while in transit, but do not confirm ownership and authenticity when the video is viewed. In response to this lacuna, researchers have largely studied digital video watermarking, where information is invisibly inserted in video streams to offer copyright protection, authentication, and traceability. A large array of methods has been suggested over the years, which involve spatial-domain strategies like LSB and correlation-based embedding, frequency-domain methods involving DCT, DWT, and SVD, and compressed-domain watermarking with integration of standards like MPEG, H.264, and H.265. Recently, hybrid schemes and deep learning-based methods have been proposed to improve resistance against geometric attacks, desynchronization, and compression while providing imperceptibility. This paper provides a comprehensive overview of these methods, comparing their merits and demerits in various applications. It further explores key performance metrics like PSNR, SSIM, NCC, BER, and BCR, along with the security of watermarking systems against different classes of attacks. Through integration of existing work and an identification of emerging trends such as AI-based adaptive watermarking, blockchain-enabled ownership verification, and codec-aware embedding, this research offers a complete guide for further developing safe, efficient, and viable video watermarking schemes.</p>

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A Comprehensive Review of Digital Video Watermarking Techniques in Spatial, Frequency, and Compressed Domain

  • 期刊论文

  • 发表日期：

    2025年10月29日

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  • Riddhi,
  • Preeti Garg,
  • Vineet Sharma

摘要

As there has been an exponential growth of video watching on digital media, protecting multimedia content against piracy, forgery, and unauthorized sharing has become an essential challenge. Conventional cryptographic techniques secure data only while in transit, but do not confirm ownership and authenticity when the video is viewed. In response to this lacuna, researchers have largely studied digital video watermarking, where information is invisibly inserted in video streams to offer copyright protection, authentication, and traceability. A large array of methods has been suggested over the years, which involve spatial-domain strategies like LSB and correlation-based embedding, frequency-domain methods involving DCT, DWT, and SVD, and compressed-domain watermarking with integration of standards like MPEG, H.264, and H.265. Recently, hybrid schemes and deep learning-based methods have been proposed to improve resistance against geometric attacks, desynchronization, and compression while providing imperceptibility. This paper provides a comprehensive overview of these methods, comparing their merits and demerits in various applications. It further explores key performance metrics like PSNR, SSIM, NCC, BER, and BCR, along with the security of watermarking systems against different classes of attacks. Through integration of existing work and an identification of emerging trends such as AI-based adaptive watermarking, blockchain-enabled ownership verification, and codec-aware embedding, this research offers a complete guide for further developing safe, efficient, and viable video watermarking schemes.