Visible to the public A novel SVD and online sequential extreme learning machine based watermark method for copyright protection

TitleA novel SVD and online sequential extreme learning machine based watermark method for copyright protection
Publication TypeConference Paper
Year of Publication2017
AuthorsDabas, N., Singh, R. P., Kher, G., Chaudhary, V.
Conference Name2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)
ISBN Number978-1-5090-3038-5
KeywordsBER, Bit error rate, blind digital watermark algorithm, Collaboration, composability, Computer science, copyright, copyright protection, discrete wavelet transforms, Electronic mail, Image coding, image watermarking, intellectual property, ip protection, IWT, IWT domain, learning (artificial intelligence), online sequential extreme learning machine based watermark method, original host image, OSELM, policy, policy-based governance, PSNR, pubcrawl, Resiliency, singular value decomposition, SVD, Tools, Training, watermarked image, Watermarking, watermarking scheme

For the increasing use of internet, it is equally important to protect the intellectual property. And for the protection of copyright, a blind digital watermark algorithm with SVD and OSELM in the IWT domain has been proposed. During the embedding process, SVD has been applied to the coefficient blocks to get the singular values in the IWT domain. Singular values are modulated to embed the watermark in the host image. Online sequential extreme learning machine is trained to learn the relationship between the original coefficient and the corresponding watermarked version. During the extraction process, this trained OSELM is used to extract the embedded watermark logo blindly as no original host image is required during this process. The watermarked image is altered using various attacks like blurring, noise, sharpening, rotation and cropping. The experimental results show that the proposed watermarking scheme is robust against various attacks. The extracted watermark has very much similarity with the original watermark and works good to prove the ownership.

Citation Keydabas_novel_2017