Blind Image Watermark Analysis Using Feature Fusion and Neural Network Classifier

Authors:
Wei Lu;Wei Sun;Hongtao Lu
Affiliations:
School of Information Science and Technology and Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, China 510275;School of Software and Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, China 510275;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China 200240
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Year:
2008

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Abstract

Over the past two decades, great efforts have been made to develop digital watermarking techniques for multimedia copyright protection and authentication. However, most of watermark detection methods are designed based on the corresponding specific watermark embedding procedures. In this paper, we propose a general blind watermarking analysis scheme to recognize whether images are watermarked no matter what kind of watermark embedding schemes are used. In the proposed method, multiscale feature fusion are used to construct statistical characteristics between non-watermarked images and watermarked images. Then, RBF neural networks are used to classify these characteristics. Numerical simulations show that the proposed scheme describes intrinsic statistical characteristics and the proposed blind watermark analysis method is effective.