ELM-Kernel and reduced kernel ELM based watermarking scheme

Abstract

With high popularity of Internet, digital data or information can be shared at much higher speed, with less expenses and efforts. This creates new opportunities like e-advertisement, web publishing, audio and video deliveries but these opportunities create many new threats also. An important issue is the copyright protection of the content. Digital watermarking is one of the approach gaining interest to achieve the security of the content. For copyright protection, the blind digital watermarking schemes with the new machine learning technique ELM-Kernel(KELM) and Reduced Kernel Extreme Learning Machine(RKELM) plus Integer Wavelet Transformation(IWT) and Singular Value Decomposition(SVD) have been proposed. The scheme is composed of three steps, namely, watermark embedding, machine learning phase and finally an extraction of watermark. In the embedding phase, the cover image is transformed into IWT domain, then the HL sub-band has been divided into 4 × 4 non-overlapping blocks. SVD is applied to each block to get their singular values then these values have been modified by using the Cox’s formula to embed one bit of watermark in one block and a watermarked image is then obtained. During the machine training phase, an ELM-Kernel(KELM) and Reduced Kernel Extreme Learning Machine(RKELM) is trained separately to memorize the relationship between the coefficients of watermarked image and the original cover image. During extraction phase, an oblivious phase, where the original image is not required and the trained machine is used to extract the watermark from the watermarked image. This algorithm aims to minimize the value of BER in order to assess the robustness of the algorithm. Results show that value of BER is very small even watermarked images are subject to attacks like noise, cropping, sharpening, rotation and blurring. The extracted watermarks are shown in the results which has great similarity with the original watermark. Performance of the proposed method is analyzed by the comparative study with some of the existing methods and it demonstrates that the proposed method outperforms these methods in terms of robustness.