Embedding and non-blind extraction of watermark data in images in FRFT domain

2009 ◽  
Author(s):  
F. Hussain ◽  
E. Khan ◽  
O. Farooq
Keyword(s):  
Blind Extraction

A Novel Blind Watermark Extraction Algorithm Based on Nonsubsampled Contourlet Transform

2014 ◽  
Vol 989-994 ◽  
pp. 1901-1904
Author(s):  
Lei Feng ◽  
Xiao Fei Shi ◽  
Hong Yu Chen ◽  
Yan Hua Li ◽  
Yue Long Zhang
Keyword(s):  
Prior Knowledge ◽  
Blind Source Separation ◽  
Extraction Method ◽  
Low Frequency ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Independent Components ◽  
Extraction Algorithm ◽  
Blind Extraction ◽  
Ica Algorithm

Most existing watermark extraction algorithms were dependent on prior knowledge. This paper proposed a blind extraction method without relying on prior knowledge. According to constructing new observation based on nonsubsampled contourlet transform, which utilizes low frequency and directional components of watermarked image, more independent components are generated. We involve these components into watermarked image and resort this solution to multichannel blind source separation. Estimated watermark is recovered by ICA algorithm. Experiment results indicate that the proposed method can achieve better results in contrast with two existing algorithms.

A Blind Extraction Method of FH Signals Based on Improved Symmetric Co-Occurrence Matrix in HF Channel

2014 ◽  
Vol 543-547 ◽  
pp. 2570-2574
Author(s):  
Pei Gu ◽  
Bin Wang
Keyword(s):  
Extraction Method ◽  
Blind Detection ◽  
Threshold Method ◽  
Time Frequency ◽  
Low Snr ◽  
Interference Signals ◽  
Noise Threshold ◽  
Blind Extraction ◽  
Occurrence Matrix ◽  
Hf Channel

To remove the noise and interference signals in the blind detection of FH signals from HF channel, this paper gives a FH signals extraction method based on the improved symmetric GLCM, by combining the time-frequency analysis with the threshold method of GLCM. Firstly, the paper defines the calculation of improved symmetric GLCM on the direction of frequency and time. Then, the noise threshold is estimated based on the noise probability in the improved GLCM of frequency and the FH signals from the improved GLCM of time can be extracted by using the noise threshold. Simulation results show that the method can extract integrated FH signals under low SNR without any prior information, and that the estimation of noise threshold is more accurate and stable. Furthermore the method is simple with a small amount of computation, which is easy to be applied in the engineering.

Sign in / Sign up

Export Citation Format

Share Document