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Countering JPEG anti-forensics based on noise level estimation

  • Hui Zeng
  • Jingjing Yu
  • Xiangui Kang
  • Siwei Lyu
Research Paper
  • 142 Downloads

Abstract

Quantization artifact and blocking artifact are the two types of well-known fingerprints of JPEG compression. Most JPEG forensic techniques are focused on these fingerprints. However, recent research shows that these fingerprints can be intentionally concealed via anti-forensics, which in turn makes current JPEG forensic methods vulnerable. A typical JPEG anti-forensic method is adding anti-forensic dither to DCT transform coefficients and erasing blocking artifact to remove the trace of compression history. To deal with this challenge in JPEG forensics, in this paper, we propose a novel countering method based on the noise level estimation to identify the uncompressed images from those forged ones. The experimental results show that the proposed method achieves superior performance on several image databases with only one-dimensional feature. It is also worth emphasizing that the proposed threshold-based method has explicit physical meaning and is simple to be implemented in practice. Moreover, we analyze the strategies available to the investigator and the forger in the case of that they are aware of the existence of each other. Game theory is used to evaluate the ultimate performance when both sides adopt their Nash equilibrium strategies.

Keywords

game theory quantization artifact blocking artifact JPEG forensics anti-forensics noise level estimation 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. U1536204, 61379155, 61332012), National Natural Science Foundation of Guangdong Province (Grant No. s2013020012788), and Special Funding for Basic Scientific Research of Sun Yat-sen University (Grant No. 6177060230).

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Hui Zeng
    • 1
  • Jingjing Yu
    • 1
  • Xiangui Kang
    • 1
  • Siwei Lyu
    • 2
  1. 1.Guangdong Key Laboratory of Information Security, School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  2. 2.Department of Computer ScienceUniversity at Albany, SUNYAlbanyUSA

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