A novel image watermarking scheme using Extreme Learning Machine

Anurag Mishra, Girija Chetty, Lavneet Singh, Amita Goel, Rampal Singh

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

38 Citations (Scopus)
4 Downloads (Pure)


In this paper, a novel digital image watermarking algorithm based on a fast neural network known as Extreme Learning Machine (ELM) for two grayscale images is proposed. The ELM algorithm is very fast and completes its training in milliseconds unlike its other counterparts such as BPN. The proposed watermarking algorithm trains the ELM by using low frequency coefficients of the grayscale host image in transform domain. The trained ELM produces a sequence of 1024 real numbers, normalized as per N(0, 1) as an output. This sequence is used as watermark to be embedded within the host image using Cox's formula to obtain the signed image. The visual quality of the signed images is evaluated by PSNR. High PSNR values indicate that the quality of signed images is quite good. The computed high value of SIM (X, X*) establishes that the extraction process is quite successful and overall the algorithm finds good practical applications, especially in situations that warrant meeting time constraints.
Original languageEnglish
Title of host publicationThe 2012 International Joint Conference on Neural Networks (IJCNN)
EditorsHussein Abbass, Daryl Essam, Ruhul Sarker
Place of PublicationBrisbane
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781467314909, 9781467314893
ISBN (Print)9781467314886
Publication statusPublished - 2012
Event2012 International Joint Conference on Neural Networks (IJCNN) - Brisbane, Brisbane, Australia
Duration: 10 Jul 201215 Jul 2012


Conference2012 International Joint Conference on Neural Networks (IJCNN)
Abbreviated titleIJCNN 2012


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