DWT-QR based blind image watermarking method using firefly algorithm

Altay, Seyma Yucel; Ulutaş, Güzin

doi:10.1109/siu49456.2020.9302216

Cited by 4 publications

(2 citation statements)

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“…PSNR helps to detect the perceptual transparency of the watermarking system, whereas NC is utilized to test robustness. Schematic diagram used for Objective function tried to be maximized in this study is as in ( 37).…”

Section: Optimization Of Threshold Values Through Famentioning

confidence: 99%

“…Optimization algorithms in a watermarking scheme are usually used to determine optimal embedding parameters or embedding positions. Some of algorithms used to find suitable parameters are Ant Colony Optimization (ACO) [28,29], Artificial Bee Colony (ABC) [30][31][32], Cuckoo Search (CS) [33], Differential Evolution (DE) [34][35][36], Firefly Algorithm (FA) [25,[37][38][39][40][41], Genetic Algorithm (GAL) [42][43][44], Particle Swarm Optimization (PSO) [45][46][47] etc. FA, inspired by the behaviors of fireflies, have been found to be more advantageous compared with other nature-inspired algorithms and to present good optimal solutions for many problems.…”

Section: Introductionmentioning

confidence: 99%

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Biometric Watermarking Schemes Based on QR Decomposition and Schur Decompositon in RIDWT Domain

Altay

Ulutaş

2023

Preprint

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In this paper, two new methods are proposed to hide a biometric-based digital watermark generated from an iris image into a color image to resolve ownership conflicts. One of them is the QR decomposition-based approach in the redistributed invariant discrete wavelet transform (RIDWT) domain, and the other is the Schur decomposition-based approach in the RIDWT domain. After the Y channel of the color image in YCbCr space is decomposed by RIDWT, the lowest frequency sub-band is divided into non-overlapping blocks. Blocks with a lower standard deviation are selected to embed each bit of the watermark created from the iris image into a block. Watermarking of selected blocks is performed with QR decomposition in the first approach and Schur decomposition in the second approach. The firefly algorithm is used to balance the robustness and perceptual transparency in hiding the watermark. Experimental results are evaluated in terms of robustness and imperceptibility to prove the effectiveness of the proposed systems. In addition, validation performances are analyzed based on false rejection rate, false acceptance rate and equal error rate. The results show that both methods have high perceptual quality and robustness, and also a very low error rate in ownership verification.

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Section: Optimization Of Threshold Values Through Famentioning

confidence: 99%