Zai-Ran Wang, Jing Dong and Wei Wang. Quantization Based Watermarking Methods Against Valumetric Distortions. International Journal of Automation and Computing, vol. 14, no. 6, pp. 672-685, 2017. https://doi.org/10.1007/s11633-016-1010-6
Citation: Zai-Ran Wang, Jing Dong and Wei Wang. Quantization Based Watermarking Methods Against Valumetric Distortions. International Journal of Automation and Computing, vol. 14, no. 6, pp. 672-685, 2017. https://doi.org/10.1007/s11633-016-1010-6

Quantization Based Watermarking Methods Against Valumetric Distortions

doi: 10.1007/s11633-016-1010-6
Funds:

National Nature Science Foundation of China U1536120

National Nature Science Foundation of China 61303262

More Information
  • Author Bio:

    Zai-Ran Wang received the B. Eng. degree in computer science and technology from the College of Information Science and Engineering, Shandong Normal University, China in 2009. He is currently Ph. D. candidate in the College of Engineering & Information Technology, University of Chinese Academy of Sciences, China.
        His research interests include digital watermarking, image processing and pattern recognition.
        E-mail:wzr1201@163.com
        ORCID iD:0000-0002-8483-7742

    Wei Wang received the B. Eng. degree in computer science and technology from North China Electric Power University, China in 2007. Since 2012, he has been with the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China, where he is currently an assistant professor.
        His research interests include pattern recognition, image processing, and digital image forensics, including watermarking, steganalysis, and tampering detection.
        E-mail:wwang@nlpr.ia.ac.cn

  • Corresponding author: Jing Dong received the B. Sc. degree in electronic information science and technology from Central South University, China in 2005, and the Ph. D. degree in pattern recognition from the Institute of Automation, Chinese Academy of Sciences, China. Since 2010, she has been with the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China, where she is currently an assistant professor. She is a member of the IEEE Computer Science Society, the Signal Society, and the IEEE Communication Society.
        Her research interests include pattern recognition, image processing, and digital image forensics, including watermarking, steganalysis, and tampering detection.
        E-mail:jdong@nlpr.ia.ac.cn (Corresponding author)
        ORCID iD:0000-0002-2763-7832
  • Received Date: 2015-06-17
  • Accepted Date: 2015-09-22
  • Publish Online: 2017-06-25
  • Publish Date: 2017-12-01
  • Most of the quantization based watermarking algorithms are very sensitive to valumetric distortions, while these distortions are regarded as common processing in audio/video analysis. In recent years, watermarking methods which can resist this kind of distortions have attracted a lot of interests. But still many proposed methods can only deal with one certain kind of valumetric distortion such as amplitude scaling attack, and fail in other kinds of valumetric distortions like constant change attack, gamma correction or contrast stretching. In this paper, we propose a simple but effective method to tackle all the three kinds of valumetric distortions. This algorithm constructs an invariant domain first by spread transform which satisfies certain constraints. Then an amplitude scale invariant watermarking scheme is applied on the constructed domain. The validity of the approach has been confirmed by applying the watermarking scheme to Gaussian host data and real images. Experimental results confirm its intrinsic invariance against amplitude scaling, constant change attack and robustness improvement against nonlinear valumetric distortions.

     

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