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<p>In this study, a novel block-based image watermarking method is proposed to enhance the quality and robustness of watermarked images. Traditional image watermarking techniques often treat the host image as a whole, leading to information loss due to the homogeneous fusion ratio applied across the entire image. To address this issue, the proposed method divides the host and watermark images into non-overlapping blocks of fixed sizes. Each block is then processed individually using artificial intelligence optimization algorithms, including Genetic Algorithm (GA), Differential Evolution Algorithm (DE), Artificial Bee Colony (ABC), and Sand Cat Swarm Optimization Algorithm (SCSO), to determine the optimal fusion coefficients. The block-based approach minimizes the contrast loss and enhances the watermark’s visibility and robustness. The application of a smoothing operator further mitigates the saw-tooth effect, commonly observed in block-based methods, leading to improved visual quality. Furthermore, the proposed method’s performance is compared against traditional watermarking techniques across various quality metrics. The findings unequivocally indicate that the proposed approach demonstrates a superior performance relative to conventional methodologies. In the comparative analysis of optimization algorithms, the SCSO exhibited a markedly enhanced performance over its counterparts, while DE algorithm also outperformed ABC and GA. These findings suggest that the proposed method provides a significant advancement over traditional techniques in the domain of image watermarking.</p>

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A novel optimized block-based approach for image watermarking

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月9日

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  • Harun Akbulut,
  • Muhammet Emin Sahin,
  • Oğuzhan Akay

摘要

In this study, a novel block-based image watermarking method is proposed to enhance the quality and robustness of watermarked images. Traditional image watermarking techniques often treat the host image as a whole, leading to information loss due to the homogeneous fusion ratio applied across the entire image. To address this issue, the proposed method divides the host and watermark images into non-overlapping blocks of fixed sizes. Each block is then processed individually using artificial intelligence optimization algorithms, including Genetic Algorithm (GA), Differential Evolution Algorithm (DE), Artificial Bee Colony (ABC), and Sand Cat Swarm Optimization Algorithm (SCSO), to determine the optimal fusion coefficients. The block-based approach minimizes the contrast loss and enhances the watermark’s visibility and robustness. The application of a smoothing operator further mitigates the saw-tooth effect, commonly observed in block-based methods, leading to improved visual quality. Furthermore, the proposed method’s performance is compared against traditional watermarking techniques across various quality metrics. The findings unequivocally indicate that the proposed approach demonstrates a superior performance relative to conventional methodologies. In the comparative analysis of optimization algorithms, the SCSO exhibited a markedly enhanced performance over its counterparts, while DE algorithm also outperformed ABC and GA. These findings suggest that the proposed method provides a significant advancement over traditional techniques in the domain of image watermarking.