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Forgery detection using polynomial fitting in recompressed JPEG images

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Abstract

Detection of double Joint photographic experts group (JPEG) compression is a major part of image forensics, especially double JPEG compression detection with the same quantization matrix is still a challenging problem. Detection of double JPEG compression for different quantization matrices has achieved good performance so far, and some methods for detecting the same quantization matrix have been proposed. However, most methods designed for grayscale images, where they utilize only truncation and rounding errors, not fully exploiting all the useful information of color JPEG images. In addition, there is no suitable model to analyze the convergence of JPEG images. These results in poor accuracy of the double compression detection for color JPEG images with the same quantization matrix. To solve this problem, we propose an effective method that utilizes third-order polynomial fitting for error convergence curves. Firstly, five successive compressions are used to extract the quantization error, dequantization error, coefficient error, and truncation error in each compression and decompression process. Secondly, based on the convergence characteristics of these four types of error information, a third-order polynomial model and least squares are applied for the fit, and then, the slope of each point of the fitted curve and the difference of the slopes are used as convergence characteristics. Moreover, the fitted curves predict the pre-compression state, which can effectively amplify the gap between single and double compression. Finally, support vector machines (SVM) are used to train and predict the final features. The experiments illustrate that the proposed method enhances accuracy by 2–3% compared to the most recent method and by approximately 15% compared to the earlier method in multiple datasets, including NRCS, MIXDATA, and MIXDATA-I.

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Funding

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61802111, 61872125, 62202141), the Science and Technology Project of Henan Province (Grant Nos. 232102210109, 232102210096), Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness (No. HNTS2022019), Pre-research Project of SongShan Laboratory (Grant No. YYJC012022011) and the Graduate Talent Program of Henan University (Grant Nos. SYLYC2022193 and SYLAL2023020).

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Authors and Affiliations

  1. School of Artificial Intelligence, Henan Engineering Research Center for Industrial Internet of Things, Henan University, Zhengzhou, 450046, China

    Xiuli Chai & Yong Tan

  2. School of Software, Intelligent Data Processing Engineering Research Center of Henan Province, Institute of Intelligent Network System, Henan University, Kaifeng, 475004, China

    Zhihua Gan

  3. School of Computer and Information Engineering, Henan University, Kaifeng, 475001, China

    Yakun Niu

  4. Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Jinwei Wang

Authors
  1. Xiuli Chai
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  2. Yong Tan
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  3. Zhihua Gan
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  4. Yakun Niu
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  5. Jinwei Wang
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Contributions

XC contributed to conceptualization, methodology, funding acquisition, writing—review and editing, and validation. YT contributed to methodology, software, writing—original draft, and writing—review and editing. ZG contributed to project administration, supervision, writing—review and editing, and funding acquisition. YN contributed to methodology, supervision, and conceptualization. JW contributed to methodology, supervision, and conceptualization. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Xiuli Chai or Zhihua Gan.

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Chai, X., Tan, Y., Gan, Z. et al. Forgery detection using polynomial fitting in recompressed JPEG images. SIViP 18, 2439–2451 (2024). https://doi.org/10.1007/s11760-023-02919-y

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  • DOI: https://doi.org/10.1007/s11760-023-02919-y

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