Image watermarking is an important extension of intellectual property protection that facilitates the identification and authentication of multimedia content. This paper aims to improve and optimize image watermarking techniques to ensure effective image protection regardless of image size or format. The proposed framework integrates discrete wavelet transform (DWT) and discrete cosine transform (DCT) to enhance watermark embedding performance and preserve data integrity. In addition, the study addresses a common challenge in watermarking systems, namely the increase in perceptible noise that may degrade visual image quality after watermark embedding. To overcome this issue, advanced noise-reduction and feature-processing strategies are incorporated, including AlexNet-based feature extraction, principal component analysis (PCA), independent component analysis (ICA), blind source separation (BSS), and optimization-assisted BSS. Extensive experiments are conducted to evaluate the effectiveness of the proposed method in terms of imperceptibility, robustness, extraction reliability, and computational efficiency. The proposed Dipper-Throated Particle Swarm Optimization (DTPSO) algorithm combined with DWT-DCT achieves a peak signal-to-noise ratio (PSNR) of 65.78 dB, a normalized cross-correlation (NCC) of 0.9189, an accuracy of 0.9766, and a bit error rate (BER) of 0.0234 on color images, demonstrating strong watermark imperceptibility and reliable extraction performance under different attack conditions.In addition, the proposed DWT + DCT+DTPSO model exhibits superior computational efficiency, achieving the lowest execution time of 46.5 s and the minimum memory consumption of 768 MB among the compared methods. These results confirm that the proposed methodology provides an effective and efficient image watermarking solution that enhances the protection, robustness, and integrity of digital multimedia content.
Khafaga et al. (Tue,) studied this question.