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<p>In today’s digital era, safeguarding important documents is crucial, especially in networked environments. Digital watermarking using biometric characteristics plays a vital role in ensuring security and authenticity and authenticity. Singular value decomposition (SVD) is widely used for image watermarking due to its simplicity and minimal impact on image feature. But SVD-based watermarking systems have several limitations, including the false positive problem (FPP) and trade-offs between imperceptibility, embedding capacity, and resilience. To address these challenges, this study integrates Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) with SVD for an enhanced hybrid watermarking approach. The proposed method securely embeds biometric information, such as fingerprints and signatures, into images, improving authenticity and protection. The model consists of three key steps: (1) efficient key generation for encryption and decryption to mitigate FPP issues, (2) biometric feature incorporation to enhance watermark authenticity, and (3) a combined DWT-DCT-SVD transformation for watermark creation, ensuring higher security. Experimental results demonstrate that the proposed technique achieves a Peak Signal-to-Noise Ratio (PSNR) above 49, Structural Similarity Index (SSIM) near 1, and Normalized Correlation Coefficient (NCC) consistently above 0.9, ensuring robustness against various attacks. Compared to existing DWT-SVD and SVD-Wang-Landau sampling methods, the proposed approach improves PSNR by 13.74% and 75.57%, respectively. The findings indicate that this method is highly durable and meets imperceptibility requirements, making it a reliable solution for secure image watermarking.</p>

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DWT-DCT-SVD: A Hybrid Image Watermarking Algorithm with FPP Resistant Enhancement

  • 期刊论文

  • 发表日期：

    2025年4月21日

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  • V. Sundhararaj,
  • B. Meenakshipriya,
  • P. Nirmala Devi,
  • K. Ezhil Vignesh

摘要

In today’s digital era, safeguarding important documents is crucial, especially in networked environments. Digital watermarking using biometric characteristics plays a vital role in ensuring security and authenticity and authenticity. Singular value decomposition (SVD) is widely used for image watermarking due to its simplicity and minimal impact on image feature. But SVD-based watermarking systems have several limitations, including the false positive problem (FPP) and trade-offs between imperceptibility, embedding capacity, and resilience. To address these challenges, this study integrates Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) with SVD for an enhanced hybrid watermarking approach. The proposed method securely embeds biometric information, such as fingerprints and signatures, into images, improving authenticity and protection. The model consists of three key steps: (1) efficient key generation for encryption and decryption to mitigate FPP issues, (2) biometric feature incorporation to enhance watermark authenticity, and (3) a combined DWT-DCT-SVD transformation for watermark creation, ensuring higher security. Experimental results demonstrate that the proposed technique achieves a Peak Signal-to-Noise Ratio (PSNR) above 49, Structural Similarity Index (SSIM) near 1, and Normalized Correlation Coefficient (NCC) consistently above 0.9, ensuring robustness against various attacks. Compared to existing DWT-SVD and SVD-Wang-Landau sampling methods, the proposed approach improves PSNR by 13.74% and 75.57%, respectively. The findings indicate that this method is highly durable and meets imperceptibility requirements, making it a reliable solution for secure image watermarking.