A Blind Digital Watermarking Approach Using Palmprint-Embedded Local Binary Patterns and Arnold Cat Map Transformations
The rapidly evolving healthcare domain and the rise of telemedicine and remote diagnostic techniques underscore the critical demand for secure, authentic, and reliable medical imaging solutions. This study introduces an innovative digital watermarking method designed to safeguard medical images against unauthorized alterations, employing biometric data—specifically, palmprints—as the watermark. Utilizing Local Binary Patterns (LBP) for feature extraction and combining Arnold Cat Map (ACM) with Discrete Cosine Transform (DCT) for image processing, this research implements a novel embedding strategy based on a 2 Least Significant Bits (2LSB) mechanism, facilitating a blind watermarking approach. The efficacy of the proposed method is validated through exceptional Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics, achieving values of 51.21675 dB and 0.9999, respectively. These metrics provide substantial evidence of the technique’s imperceptibility and robustness, employing state-of-the-art methodologies. The findings of this paper offer a compelling solution for enhancing the security of medical imagery within the telemedicine sector, promoting data integrity and supporting the verification and authentication processes.
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A Blind Digital Watermarking Approach Using Palmprint-Embedded Local Binary Patterns and Arnold Cat Map Transformations
Semantic Scholar · Computer Science · 2024
Abstract
The rapidly evolving healthcare domain and the rise of telemedicine and remote diagnostic techniques underscore the critical demand for secure, authentic, and reliable medical imaging solutions. This study introduces an innovative digital watermarking method designed to safeguard medical images against unauthorized alterations, employing biometric data—specifically, palmprints—as the watermark. Utilizing Local Binary Patterns (LBP) for feature extraction and combining Arnold Cat Map (ACM) with Discrete Cosine Transform (DCT) for image processing, this research implements a novel embedding strategy based on a 2 Least Significant Bits (2LSB) mechanism, facilitating a blind watermarking approach. The efficacy of the proposed method is validated through exceptional Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics, achieving values of 51.21675 dB and 0.9999, respectively. These metrics provide substantial evidence of the technique’s imperceptibility and robustness, employing state-of-the-art methodologies. The findings of this paper offer a compelling solution for enhancing the security of medical imagery within the telemedicine sector, promoting data integrity and supporting the verification and authentication processes.