Online Sequential Extreme Learning Machine for watermarking in DWT domain

Abstract

Protecting and securing an information of digital media is very crucial due to illegal reproduction and modification of media has become an acute problem for copyright protection now a day. A Discrete Wavelet Transform (DWT) domain based robust watermarking scheme with Extreme Learning Machine (ELM), Online Sequential Extreme Learning Machine (OSELM) and Weighted Extreme Learning Machine (WELM) have been implemented on different color images. The proposed scheme which combine DWT with ELM, OSELM and WELM machine learning methods and a watermark or a tag or a sequence is embedded as an ownership information. Experimental results demonstrate that the proposed watermarking scheme is imperceptible/transparent and robust against image processing and attacks such as blurring, cropping, JPEG, noise addition, rotation, scaling, scaling–cropping, and sharpening. Performance and efficacy of algorithms of watermarking scheme is determined by measuring Peak Signal to Noise Ratio (PSNR), Bit Error Rate (BER) and Similarity parameter SIM(X,X⁎) and calibrated results are compared with other existing machine learning methods. As a watermark detector, machine learning techniques are used to learn neighbors relationship among pixels in a natural image has high relevance to its neighbors, so this relationship can be predicted by its neighbors using machine learning methods and watermark image can be extracted and detected and thereby ownership can be verified.