Regular Articles

Stereoscopic image tamper detection and self-recovery using hierarchical detection and stereoscopic matching

[+] Author Affiliations
Wujie Zhou

Ningbo University, Faculty of Information Science and Engineering, Ningbo 315211, China

Zhejiang University of Science and Technology, School of Information and Electronic Engineering, Hangzhou 310023, China

Gangyi Jiang

Ningbo University, Faculty of Information Science and Engineering, Ningbo 315211, China

Nanjing University, National Key Lab of Software New Technology, Nanjing 210093, China

Ting Luo

Ningbo University, Faculty of Information Science and Engineering, Ningbo 315211, China

Mei Yu

Ningbo University, Faculty of Information Science and Engineering, Ningbo 315211, China

Feng Shao

Ningbo University, Faculty of Information Science and Engineering, Ningbo 315211, China

Zongju Peng

Ningbo University, Faculty of Information Science and Engineering, Ningbo 315211, China

J. Electron. Imaging. 23(2), 023022 (Apr 25, 2014). doi:10.1117/1.JEI.23.2.023022
History: Received August 20, 2013; Revised March 19, 2014; Accepted March 26, 2014
Text Size: A A A

Abstract.  We propose a new watermarking algorithm for stereoscopic image tamper detection and self-recovery in three-dimensional multimedia services. Initially, left and right views of stereoscopic image are divided into nonoverlapping 2×2 blocks in order to improve the accuracy of tamper localization in an image. As the left and right views of a stereoscopic image are not independent from each other but have an inter-view relationship, every block of a stereoscopic image is classified into matching block or nonmatching block and then block disparities are obtained. Both matching blocks in the left and right views have similar pixel values, so that fewer bits are allocated for recovery watermark generation, which can increase the quality of watermarked stereoscopic images. A hierarchical tamper-detection strategy with a four-level checkup is presented to improve the accuracy of tamper localization. Additionally, two copies of block (matching block and nonmatching block) information are embedded into the stereoscopic image, and it assures the quality of tampered recovery. For the nonmatching block recovery, two copies of the partner block are embedded into their chaotic mapping blocks, which supply the second chance for tamper recovery. For the matching block recovery, the inter-view relationship between tampers of left and right views supplies the third chance for tamper recovery. Experimental results show that the proposed algorithm can not only detect and locate tampers in stereoscopic image more accurately but also recover the tampered regions better, compared with other algorithms.

Figures in this Article
© 2014 SPIE and IS&T

Citation

Wujie Zhou ; Gangyi Jiang ; Ting Luo ; Mei Yu ; Feng Shao, et al.
"Stereoscopic image tamper detection and self-recovery using hierarchical detection and stereoscopic matching", J. Electron. Imaging. 23(2), 023022 (Apr 25, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.2.023022


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.