Exposing Image Tampering with the Same Quantization Matrix

  • Qingzhong  Liu
  • Andrew H. Sung
  • Zhongxue Chen
  • Lei Chen
Chapter

Abstract

Image tampering, being readily facilitated and proliferated by today’s digital techniques, is increasingly causing problems regarding the authenticity of images. As the most popular multimedia data, JPEG images can be easily tamperedwithout leaving any clues; therefore, JPEG-basedforensics, including the detection of double compression, interpolation, rotation, etc., has become an active research topic in multimedia forensics. Nevertheless, the interesting issue of detecting image tampering and its related operations by using the same quantization matrix has not been fully investigated. Aiming to detect such forgery manipulations under the same quantization matrix, we propose a detection method by using shift-recompression-based reshuffle characteristic features. Learning classifiers are applied to evaluating the efficacy. Our experimental results indicate that the approach is indeed highly effective in detecting image tampering and relevant manipulations with the same quantization matrix.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Qingzhong  Liu
    • 1
  • Andrew H. Sung
    • 2
  • Zhongxue Chen
    • 3
  • Lei Chen
    • 1
  1. 1.Department of Computer ScienceSam Houston State UniversityHuntsvilleUSA
  2. 2.School of ComputingThe University of Southern MississippiHattiesburgUSA
  3. 3.Department of Epidemiology and BiostatisticsIndiana University BloomingtonBloomingtonUSA

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