Lifting Wavelet Transform based Fast Watermarking of Video Summaries using Extreme Learning Machine

Anurag Mishra, Charu Agarwal, Girija CHETTY

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

In this paper, we present a robust semi-blind video watermarking scheme in lifting wavelet transform (LWT) domain using Extreme Learning Machine (ELM). In this scheme, first the static video summary is generated using extraction of color features from video frames. Second, the frames comprised of video summary are watermarked in LWT domain. To develop a robust and real time watermarking scheme, a fast Single hidden Layer Feedforward Neural Network (SLFN) known as ELM is used for watermark embedding and extraction. To evaluate the performance of the present scheme, several signal processing attacks are applied to each watermarked frame. Experimental evidence shows that the proposed scheme is robust against selected attacks. Due to fast processing of frames, the proposed scheme is also found to be suitable for real time watermarking of video.
Original languageEnglish
Title of host publicationProceedings 2018 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationRio de Janeiro, Brazil
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-7
Number of pages7
ISBN (Electronic)9781509060146
ISBN (Print)9781509060153
DOIs
Publication statusPublished - 8 Jul 2018
EventIEEE International Conference on Neural Networks -
Duration: 1 Jan 2011 → …

Conference

ConferenceIEEE International Conference on Neural Networks
Abbreviated titleICNN
Period1/01/11 → …

Fingerprint Dive into the research topics of 'Lifting Wavelet Transform based Fast Watermarking of Video Summaries using Extreme Learning Machine'. Together they form a unique fingerprint.

  • Cite this

    Mishra, A., Agarwal, C., & CHETTY, G. (2018). Lifting Wavelet Transform based Fast Watermarking of Video Summaries using Extreme Learning Machine. In Proceedings 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2018.8489305