Biometric Embedded Non-Blind Color Image Watermarking with Geometric Tamper Resistance via SIFT-ORB Keypoint Matching

This work introduces a non-blind watermarking framework for color images to address tamper detection, particularly under geometric transformations. The proposed scheme fuses two watermarks, a personal signature and a biometric fingerprint, into a unified composite watermark embedded into the chrominance component of the cover image using a multi-level transform domain approach, discrete wavelet transforms (DWTs), discrete cosine transforms (DCTs), and singular value decomposition (SVD). By leveraging the rotation-invariant properties of scale-invariant feature transform (SIFT) and oriented FAST and rotated BRIEF (ORB) descriptors, the framework ensures robust tamper detection without requiring alignment, thus mitigating the limitations of conventional detection techniques vulnerable to transformation-induced tamper obfuscation (TITO). Extensive experimentation demonstrates that the method maintains high perceptual fidelity, achieving PSNR values ranging from 50 to 55 dB for embedding strength factor μ (0.01–0.04) and SSIM indices near 1 across multiple benchmark images. Furthermore, the scheme exhibits notable resilience to a range of image processing attacks and geometric distortion. Comparative evaluation reveals its superiority over existing grayscale, color, SIFT-based and DWT-DCT-SVD-based watermarking techniques, affirming its applicability in scenarios demanding secure, imperceptible, and transformation-invariant image watermarking.

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Publication Details

Journal
Computers
Published
2026-04-22
DOI
https://doi.org/10.3390/computers15050264
Primary Topic
Advanced Steganography and Watermarking Techniques
Type
article
Field-Weighted Citation Impact
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article

Biometric Embedded Non-Blind Color Image Watermarking with Geometric Tamper Resistance via SIFT-ORB Keypoint Matching

Riyanka Manna, Adıtya Kumar Sahu, Amine Khaldi, Swapnaneel Dhar
Computers
Advanced Steganography and Watermarking Techniques
article

Biometric Embedded Non-Blind Color Image Watermarking with Geometric Tamper Resistance via SIFT-ORB Keypoint Matching

Riyanka Manna, Adıtya Kumar Sahu, Amine Khaldi, Swapnaneel Dhar
article en

Abstract

This work introduces a non-blind watermarking framework for color images to address tamper detection, particularly under geometric transformations. The proposed scheme fuses two watermarks, a personal signature and a biometric fingerprint, into a unified composite watermark embedded into the chrominance component of the cover image using a multi-level transform domain approach, discrete wavelet transforms (DWTs), discrete cosine transforms (DCTs), and singular value decomposition (SVD). By leveraging the rotation-invariant properties of scale-invariant feature transform (SIFT) and oriented FAST and rotated BRIEF (ORB) descriptors, the framework ensures robust tamper detection without requiring alignment, thus mitigating the limitations of conventional detection techniques vulnerable to transformation-induced tamper obfuscation (TITO). Extensive experimentation demonstrates that the method maintains high perceptual fidelity, achieving PSNR values ranging from 50 to 55 dB for embedding strength factor μ (0.01–0.04) and SSIM indices near 1 across multiple benchmark images. Furthermore, the scheme exhibits notable resilience to a range of image processing attacks and geometric distortion. Comparative evaluation reveals its superiority over existing grayscale, color, SIFT-based and DWT-DCT-SVD-based watermarking techniques, affirming its applicability in scenarios demanding secure, imperceptible, and transformation-invariant image watermarking.

ComputersVol. 15(5)
University of Ouargla (DZ), SRM University (IN), Amrita Vishwa Vidyapeetham (IN)
Openalex Percentile: Top 12%
Advanced Steganography and Watermarking Techniques
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Biometric Embedded Non-Blind Color Image Watermarking with Geometric Tamper Resistance via SIFT-ORB Keypoint Matching — Riyanka Manna, Adıtya Kumar Sahu, et al. · Computers (2026) | TGRS Research Map | TGRS