Robust Watermarking Algorithm Based on QGT and Neighborhood Coefficient Statistical Features

Abstract

The exponential advancement of the Internet of Things and artificial intelligence technologies has significantly accelerated digital content generation and dissemination, intensifying challenges in copyright protection, identity theft, and privacy breaches. Traditional digital watermarking techniques, constrained by vulnerabilities to geometric attacks and perceptual distortions, fail to meet the demands of modern complex application scenarios. To address these limitations, this paper proposes a robust watermarking algorithm based on quaternion Gyrator transform and neighborhood coefficient statistical features, designed to enhance copyright protection efficacy. The methodology involves three key innovations: (1) The host image is partitioned into non-overlapping sub-blocks, with an inhomogeneity metric calculated from local texture and edge characteristics to prioritize embedding sequence optimization; (2) quaternion Gyrator transform is applied to each sub-block, where the real component of transformed coefficients is utilized as the feature carrier, harnessing the geometric invariance of quaternion transformations to mitigate distortions induced by rotational attacks; (3) Integration of an Improved Uniform Log-Polar Mapping algorithm to embed synchronization markers, reinforcing resistance to geometric attacks by preserving structural consistency under affine transformations. Prior to embedding, dynamic statistical analysis of neighborhood coefficients adjusts watermark intensity, ensuring compatibility with human visual system masking properties. Experimental results demonstrate dual advantages: The PSNR of the proposed method is 41.4921, showing good invisibility. The average NC value remains at around 0.9, demonstrating good robustness. The effectiveness and practicability of the algorithm in a complex attack environment have been verified.