A reinforcement learning for robust image watermarking based on IWT and Schur decomposition - Nurul Ain Nafisah, Muhamad; Ferda, Ernawan; Anis Farihan, Mat Raffei; Norfaradilla, Wahid (Politeknik Negeri Padang, 2025)
Copyright infringement becomes a significant concern for the creators, which has the rights to the original multimedia data. Image watermarking is increasingly critical in addressing challenges associated with copyright infringement and ensuring data integrity in multimedia technologies. Image watermarking is essential for safeguarding intellectual property and maintaining the authenticity of digital images. This study presents a reinforcement learning-driven embedding framework for robust image watermarking, utilizing the Integer Wavelet Transform (IWT) and Schur decomposition. The proposed embedding using reinforcement learning aims to enhance watermark imperceptibility and robustness against various attacks. The watermark embedding process involves decomposing the host image using IWT, applying Schur decomposition to selected blocks, and optimizing the embedding process through reinforcement learning. This dynamic embedding of a watermark improves robustness while maintaining imperceptibility. The experiments have been tested under various attacks, including noise attacks, compression, and filtered images. Experimental results demonstrate that the proposed method achieves a PSNR of 32.29 dB and an SSIM of 0.9828, indicating high imperceptibility. Robustness tests indicate a high NC of 0.9977 under JPEG2000 compression (5:1) and 0.8695 under cropping attacks (50%), outperforming existing methods. These findings suggest that the proposed scheme is a viable solution for practical digital copyright protection applications. The results indicate that the proposed scheme not only enhances robust copyright protection but also ensures the quality of the watermarked image.