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Non-aligned double JPEG compression detection based on refined Markov features in QDCT domain

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Abstract

Due to the widespread use of the JPEG format, non-aligned double JPEG (NA-DJPEG) compression is very common in image tampering. Therefore, non-aligned double JPEG compression detection has attracted significant attention in digital forensics in recent years. In most of the previous detection algorithms, grayscale images are used directly, or color images are first converted into grayscale images and then processed. However, it is worth noting that most tampered images are color images. To make full use of the color information in images, a detection algorithm, which uses color images directly, is put forward in this paper. The algorithm based on refined Markov in quaternion discrete cosine transform (QDCT) domain is proposed for NA-DJPEG compression detection. Firstly, color information of a given JPEG image is extracted from blocked images to construct quaternion, and then block image QDCT coefficient matrices, including amplitude and three angles (\(\psi \), \(\phi \), and \(\theta \)) can be obtained. Secondly, the refined Markov features are generated from the transition probability matrix in the corresponding refinement process. Our proposed refinement method not only reduces redundant features but also makes the acquired features more efficient in detection. Therefore, the refined Markov features can not only capture the intra-block correlation between block QDCT coefficients but also improve computing efficiency in real-time. Finally, support vector machine (SVM) method is employed for NA-DJPEG compression detection. The experiment results demonstrate that the proposed algorithm not only make use of color information of images, but also can achieve better detection performance with small size images (i.e., \(64 \times 64\)) outperforming state-of-the-art detection methods tested on the same dataset.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grants (Nos. 61702235, 61772281, U1636117, U1636219, 61502241, 61272421, 61232016, 61402235, 61572258 and 61702235), in part by the National Key R&D Program of China (Grant No. 2016YFB0801303 and 2016QY01W0105), in part by the plan for Scientific Talent of Henan Province (Grant No. 2018J R0018), in part by the Natural Science Foundation of Jiangsu Province, China under Grant BK20141006, and the PAPD fund.

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Authors and Affiliations

  1. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

    Jinwei Wang, Wei Huang & Sunil Kr. Jha

  2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China

    Jinwei Wang

  3. Shanxi Key Laboratory of Network and System Security, Xidian University, Shanxi, China

    Jinwei Wang

  4. State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, Henan, China

    Jinwei Wang & Xiangyang Luo

  5. New Jersey Institute of Technology, Newark, NJ, USA

    Yun-Qing Shi

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  1. Jinwei Wang
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  2. Wei Huang
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  3. Xiangyang Luo
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  5. Sunil Kr. Jha
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Correspondence to Xiangyang Luo.

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Wang, J., Huang, W., Luo, X. et al. Non-aligned double JPEG compression detection based on refined Markov features in QDCT domain. J Real-Time Image Proc 17, 7–16 (2020). https://doi.org/10.1007/s11554-019-00929-z

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