Multiple scaling factors based Semi-Blind watermarking of grayscale images using OS-ELM neural network
Ankit Rajpal, Anurag Kumar Mishra, Rajni Bala · 2016
In this paper, a multiple scaling factor based Semi-Blind watermarking scheme for grayscale image watermarking using Online Sequential Extreme Learning Machine (OS-ELM) is proposed. Four-level DWT is applied on three standard test images of size 512 × 512. LL4 sub-band coefficients are chosen for watermark embedding. OS-ELM is initially tuned with a fixed number of training data used in its initial phase and size of data block learned in each iteration. The training set is developed by combining the quantized and desired LL4 sub-band coefficients. This training set is fed to OS-ELM for training. The output of OS-ELM is a sequence of predicted coefficients which is sorted and divided into three equal parts. Multiple Scaling Factor (MSF) scheme is used for embedding a binary watermark in a Semi-Blind manner. Results show good visual quality of signed images and good similarity between the extracted and original watermarks from signed and attacked images. The PSNR and BER are found to be well optimized both in case of signed and attacked images. The time for embedding and extraction is in milliseconds, which makes the proposed technique effective for developing real time watermarking applications.