Enhanced robust image watermarking using advanced wavelet transforms and deep learning-based optimization

Digital watermarking is particularly crucial for keeping multimedia content safe from the security threats that are always changing. This study presents an innovative wavelet transform-based picture watermarking technology that employs deep learning optimization algorithms to attain exceptional robustness and invisibility. It has been suggested to use DTCWT for great shift invariance and multi-resolution analysis, together with a CNN that has been trained to smartly optimize how watermarks are added and removed. This strategy works well against new threats like machine learning-based adversarial attacks and modern compression methods like HEVC because it uses a CNN's capacity to understand complicated distortions. We tested our method on a number of datasets, such as COCO and ImageNet, and found that it works better than many classic wavelet-based methods when it comes to PSNR, SSIM, besides BER. This paper shows how hybrid methods might improve digital watermarking technology, making it possible to use it in real time on devices with limited resources, such IoT and mobile platforms.