An enhanced digital image watermarking technique using DWT-HD-SVD and deep convolutional neural network
Manish Rai, Sachin Goyal, Mahesh Kumar Pawar · International Journal of Critical Computer-Based Systems · 2023
This paper proposes a novel image watermarking model, which combines discrete wavelet transform (DWT), Hessenberg decomposition (HD), singular value decomposition (SVD)-based deep convolutional neural networks (D-CNN) technique to explore the subjective and objective quality of the images. Initially, the source and cover image are preprocessed using random sampling techniques. During the process of embedding a watermark image, the cover image is decomposed into a number of sub-bands using the DWT process and the resulting coefficients are fed into the HD process. In continuation to it, the source image is operated on the SVD simultaneously and finally, the cover image is embedded into the source image by the attack-defending process. The probability of data loss during the watermarking extraction process and this issue is postulated by the D-CNN technique that explores the denoising process on the extracted watermarked images. The experimental results show that the proposed method has a good trade-off between robustness and invisibility even for the watermarks with multiple sizes.