NDLI: A novel blind watermarking scheme based on neural networks for image

Content Provider	IEEE Xplore Digital Library
Author	Yonghong Chen Jiancong Chen
Copyright Year	2010
Description	Author affiliation: College of Computer Science & Technology, Huaqiao University, Xiamen, China (Yonghong Chen) \|\| Dept. of Computer & mathematics, Chongqing Normal University, China (Jiancong Chen)
Abstract	In this paper, a novel blind watermarking scheme based on the back-propagation neural networks (BPNN) for image is presented. First, the convolutional codes encoding is used to refine the watermark for increasing robustness of the scheme. BPNN is developed to memorize the relationships between the wavelet selected samples and a processed chaotic sequence. With wavelet domain of original image being divided into watermarking blocks, then several different BPNN models of selected watermarking blocks are trained simultaneously to form certain relationships, which are employed for embedding the coded watermark bit stream. Compared with conventional watermarking, the proposed scheme based on the trained BPNN models modifies only a small amount of image data such that the distortion on original image is imperceptible. Experimental results demonstrate the high robustness of the proposed scheme against common signal processing.
Starting Page	548
Ending Page	552
File Size	305301
Page Count	5
File Format	PDF
ISBN	9781424469420
e-ISBN	9781424469437
DOI	10.1109/ICITIS.2010.5689534
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training BPNN Blind Watermarking Watermarking Artificial neural networks Signal processing Neural Networks Robustness Biological neural networks
Content Type	Text
Resource Type	Article

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