DocumentCode :
2769083
Title :
A novel image watermarking scheme using Extreme Learning Machine
Author :
Mishra, Anurag ; Goel, Amita ; Singh, Rampal ; Chetty, Girija ; Singh, Lavneet
Author_Institution :
Dept. of Electron., Univ. of Delhi, New Delhi, India
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
6
Abstract :
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.
Keywords :
image watermarking; learning (artificial intelligence); neural nets; transforms; Cox formula; ELM; extreme learning machine; fast neural network; grayscale images; low frequency coefficients; novel digital image watermarking algorithm; novel image watermarking scheme; transform domain; Discrete cosine transforms; Machine learning; Mathematical model; Neurons; Training; Vectors; Watermarking; Digital Image Watermarking; Extreme Learning Machine (ELM); Real time applications; SIM(X, X*); Watermark Extraction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
ISSN :
2161-4393
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
Type :
conf
DOI :
10.1109/IJCNN.2012.6252363
Filename :
6252363
Link To Document :
بازگشت