A Novel Framework for Damage-Free Data Extraction in Secure Watermarking Systems

Securing sensitive data in the era of digital transformation presents significant challenges, particularly in multimedia content. While watermarking has emerged as a solution, existing techniques often compromise on imperceptibility, robustness, or reversibility. This study presents a new framework for secure, non-destructive, and reversible watermarking that integrates sophisticated cryptographic techniques with auxiliary information to guarantee flawless reconstruction of the original media while ensuring robust security. The framework employs a two-layer approach: encryption for watermark concealment and auxiliary data to support reversibility. Performance metrics, such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), bit error rate (BER), mean absolute error (MAE), and root mean square error (RMSE), were used to evaluate the effectiveness of the proposed system. Experimental results demonstrate the system’s effectiveness, achieving a mean PSNR of 50 dB, SSIM of 0.98, and minimal perceptual distortion. The system is robust to attacks such as JPEG compression (50%), Gaussian noise (variance 0.01), and 10% cropping, with a low BER (mostly under 0.02). The framework operates efficiently, with embedding times (ETs) of just 0.25[Formula: see text]s for [Formula: see text] images, making it suitable for real-time applications. This study presents a dependable and secure watermarking approach, showcasing improvements in security, reversibility, durability, and computational efficiency. Future efforts seek to increase the framework’s flexibility for intricate media, including audio and 3D content, while strengthening its ability to withstand new threats such as adversarial attacks and deepfakes.

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Publication Details

Journal
Journal of Multiscale Modelling
Published
2026-02-12
DOI
https://doi.org/10.1142/s1756973726400263
Primary Topic
Advanced Steganography and Watermarking Techniques
Type
article
Field-Weighted Citation Impact
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article

A Novel Framework for Damage-Free Data Extraction in Secure Watermarking Systems

Meesala Sudhir Kumar, Shoyeb Karim Pathan
Journal of Multiscale Modelling
Advanced Steganography and Watermarking Techniques
article

A Novel Framework for Damage-Free Data Extraction in Secure Watermarking Systems

Meesala Sudhir Kumar, Shoyeb Karim Pathan
article en

Abstract

Securing sensitive data in the era of digital transformation presents significant challenges, particularly in multimedia content. While watermarking has emerged as a solution, existing techniques often compromise on imperceptibility, robustness, or reversibility. This study presents a new framework for secure, non-destructive, and reversible watermarking that integrates sophisticated cryptographic techniques with auxiliary information to guarantee flawless reconstruction of the original media while ensuring robust security. The framework employs a two-layer approach: encryption for watermark concealment and auxiliary data to support reversibility. Performance metrics, such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), bit error rate (BER), mean absolute error (MAE), and root mean square error (RMSE), were used to evaluate the effectiveness of the proposed system. Experimental results demonstrate the system’s effectiveness, achieving a mean PSNR of 50 dB, SSIM of 0.98, and minimal perceptual distortion. The system is robust to attacks such as JPEG compression (50%), Gaussian noise (variance 0.01), and 10% cropping, with a low BER (mostly under 0.02). The framework operates efficiently, with embedding times (ETs) of just 0.25[Formula: see text]s for [Formula: see text] images, making it suitable for real-time applications. This study presents a dependable and secure watermarking approach, showcasing improvements in security, reversibility, durability, and computational efficiency. Future efforts seek to increase the framework’s flexibility for intricate media, including audio and 3D content, while strengthening its ability to withstand new threats such as adversarial attacks and deepfakes.

Journal of Multiscale Modelling
Sandip Foundation (IN)
Openalex Percentile: Top 59%
Advanced Steganography and Watermarking Techniques
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