Dictionary Learning Based Digital Image Watermarking by Using KSVD Algorithm

The crucial hurdle faced by the watermarking technique is to maintain the steadiness corresponding to several attacks while assisting a sufficient level of security. In this study, a robust lossless sparse domain-based watermarking approach combined with discrete cosine transform (DCT) is introduced to hide the secret message in the selected significant sparse elements of the host image. The proposed method takes advantage of a sparse representation-based dictionary learning process. To enhance the security of the original image, the authors first apply the DCT on a secret message. These DCT coefficients with some regularised parameters will be inserted into the selected significant sparse coefficients. At the extraction stage, the secret message is extracted from those significant sparse coefficients by employing the sparse domain orthogonal matching pursuit algorithm. Finally, the inverse DCT is applied to extract the secret message without any information loss. To show the effectiveness of the proposed method, different commonly used attacks are simulated. Simulation results in terms of peak signal-to-noise ratio, structural similarity, normal correlation, and feature similarity indicate that the proposed method can recover the hidden secret message accurately against seven different types of attacks including speckle, Gaussian, salt and pepper, rotate, crop, fold, and blur attack.