Image self-embedding with high-quality restoration capability

doi:10.1016/j.dsp.2010.04.006

Digital Signal Processing

Volume 21, Issue 2, March 2011, Pages 278-286

https://doi.org/10.1016/j.dsp.2010.04.006 Get rights and content

Abstract

In this paper, we present a new fragile watermarking method aimed at providing improved restoration capability. A mechanism of block classification is used to compress the original image according to the DCT coefficients, in which blocks corresponding to different types are encoded to variable lengths. The compressed bits are expanded as reference bits, and then embedded into the entire image along with some authentication bits generated from each block. On the receiving side, the authentication bits are extracted to localize the tampered areas, and the reference bits are used to reconstruct a reference image for restoring the contents of the tampered regions. Results show that the proposed method provides a better restoration quality.

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Zhenxing Qian received the B.S. degree in 2003 and the Ph.D. degree in 2007 from University of Science & Technology of China (USTC). Since 2009, he has been with the faculty of the School of Communication and Information Engineering, Shanghai University. His research interests include data hiding, image processing, and digital forensics.

References (8)

S. Suthaharan
Fragile image watermarking using a gradient image for improved localization and security
Pattern Recognition Lett.
(2004)
A. Cheddad
A secure and improved self-embedding algorithm to combat digital document forgery
Signal Process.
(2009)
X. Zhu et al.
A new semi-fragile image watermarking with robust tampering restoration using irregular sampling
Signal Process. Image Commun.
(2007)
H. He
Adjacent-block based statistical detection method for self-embedding watermarking techniques
Signal Process.
(2009)

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Cited by (95)

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The aim of designing self-embedding fragile watermarking is copy-right protection, tamper localization and image recovery. This paper proposes a self-embedding fragile watermarking scheme for tamper detection and recovery based on local image characteristics using Absolute Moment Block Truncation Coding (AMBTC) and fuzzy logic. The original image is divided into $(4 \times 4)$ non-overlapping blocks. The watermark of each block are generated from 6 MSBs of each pixels of the block. Using similarity matrix, the blocks are categorized into smooth and complex depending on similarity among neighboring pixels. In smooth block, mean is used as recovery data for their high similarity existence whereas complex block uses AMBTC image as it has a compressed representation of an image and preserves the features of the cover image. Recovery data are embedded into 2 LSBs of corresponding mapped block. The efficiency of the proposed scheme is analyzed in terms of Tamper Detection Rate (TDR) and Tamper Recovery Rate (TRR). The performance of the proposed scheme is analyzed against different attacks applied on different regions of the watermarked image. The experiment results show that the imperceptibility and visual quality of the watermarked image as well as recovered image is satisfactorily high even at $50 %$ tampering rate.
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Guorui Feng received the B.S. and M.S. degrees in computational mathematics from Jilin University, Changchun, P.R. China, in 1998 and 2001, respectively, and Ph.D. degree in Electronic Engineering from Shanghai Jiaotong University, Shanghai, P.R. China in 2005. From March 2006 to December 2006, he was an assistant professor in East China Normal University. From January 2007 to December 2007, he was a Research Fellow in Nanyang Technological University. From July 2008 till now, he was been working as an assistant professor in Shanghai University, P.R. China. His research interests are in the areas of signal processing and computational intelligence.

Xinpeng Zhang received the B.S. degree in computational mathematics from Jilin University, China, in 1995, and the M.E. and Ph.D. degrees in communication and information system from Shanghai University, China, in 2001 and 2004, respectively. Since 2004, he has been with the faculty of the School of Communication and Information Engineering, Shanghai University, where he is currently a Professor. His research interests include information hiding, image processing and digital forensics. He has published more than 100 papers in these areas.

Shuozhong Wang received the B.S. degree from Peking University, Beijing, China, in 1966 and the Ph.D. degree from University of Birmingham, Birmingham, England, in 1982. Currently, he is a Professor of Shanghai University. His research interests include image processing, audio processing, and information hiding.

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Image self-embedding with high-quality restoration capability

Abstract

Section snippets

Pattern Recognition Lett.

Signal Process.

Signal Process. Image Commun.

Signal Process.