TY - GEN
T1 - Lifting Wavelet Transform based Fast Watermarking of Video Summaries using Extreme Learning Machine
AU - Mishra, Anurag
AU - Agarwal, Charu
AU - CHETTY, Girija
N1 - Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/7/8
Y1 - 2018/7/8
N2 - 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.
AB - 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.
KW - Watermarking
KW - Wavelet transforms
KW - Extreme Learning Machine (ELM)
UR - http://www.scopus.com/inward/record.url?scp=85056546203&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2018.8489305
DO - 10.1109/IJCNN.2018.8489305
M3 - Conference contribution
SN - 9781509060153
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 1
EP - 7
BT - Proceedings 2018 International Joint Conference on Neural Networks (IJCNN)
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - Rio de Janeiro, Brazil
T2 - IEEE International Conference on Neural Networks
Y2 - 1 January 2011
ER -