Forgery detection using polynomial fitting in recompressed JPEG images | Signal, Image and Video Processing Skip to main content
Springer Nature Link
Log in

Forgery detection using polynomial fitting in recompressed JPEG images

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

Data will be made available on request.

References

  1. Chai, X.-L., Song, S., Tan, Y., Lei, Y., Huang, Z., Lu, Y., Wei, T.: Three-stage image forgery localization with shallow feature enhancement and attention. J. Electron. Imaging 31, 063051–063051 (2022)

    Article  Google Scholar 

  2. Zhang, J., Wang, Y., Guo, L., Luo, J., Xu, T., Wang, Y., Wang, Z., Qin, H.: Unified learning-based lossy and lossless jpeg recompression. In: 2023 IEEE International Conference on Image Processing (ICIP), pp. 216–220 (2023). https://doi.org/10.1109/ICIP49359.2023.10222354

  3. Visavakitcharoen, A., Kobayashi, H., Shiota, S., Kiya, H.: Compression performance of extended jpeg xt under various tmos. In: 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE), pp. 1–4 (2017). IEEE

  4. Ma, Y., Chai, X., Gan, Z., Zhang, Y.: Privacy-preserving tpe-based jpeg image retrieval in cloud-assisted internet of things. IEEE Internet Things J. (2023). https://doi.org/10.1109/JIOT.2023.3301042

    Article  Google Scholar 

  5. Qin, C., Hu, J., Li, F., Qian, Z., Zhang, X.: Jpeg image encryption with adaptive dc coefficient prediction and rs pair permutation. IEEE Trans. Multimed. 25, 2528–2542 (2023). https://doi.org/10.1109/TMM.2022.3148591

    Article  Google Scholar 

  6. Kubal, P., Pulgam, N., Mane, V.: Identifying image modifications using dct and jpeg quantization technique. In: 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), pp. 1–5 (2023). https://doi.org/10.1109/I2CT57861.2023.10126200

  7. Rhee, K.H.: Forensic detection of jpeg compressed image. In: 2018 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 485–488 (2018). https://doi.org/10.1109/CSCI46756.2018.00100

  8. Zhu, D., Zhou, Z.: Resampling tamper detection based on jpeg double compression. In: 2014 Fourth International Conference on Communication Systems and Network Technologies, pp. 914–918 (2014). IEEE

  9. Wei, H., Yao, H., Qin, C., Tang, Z.: Automatic forgery localization via artifacts analysis of jpeg and resampling. In: Security with Intelligent Computing and Big-Data Services: Proceedings of the Second International Conference on Security with Intelligent Computing and Big Data Services (SICBS-2018) 2, pp. 221–234 (2020). Springer

  10. Popescu, A.C., Farid, H.: Statistical tools for digital forensics. In: Fridrich, J. (ed.) Information Hiding, pp. 128–147. Springer, Berlin (2005)

    Google Scholar 

  11. Giudice, O., Guarnera, F., Paratore, A., Battiato, S.: 1-d dct domain analysis for jpeg double compression detection. In: Ricci, E., Rota Bulò, S., Snoek, C., Lanz, O., Messelodi, S., Sebe, N. (eds.) Image Analysis and Processing—ICIAP 2019, pp. 716–726. Springer, Cham (2019)

  12. Liu, H., Liu, B.: Forgery detection and tampering localization of double jpeg compression based on first digit features of dct coefficients and knr. In: 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 1134–1137 (2018). IEEE

  13. Kwon, M.-J., Yu, I.-J., Nam, S.-H., Lee, H.-K.: Cat-net: compression artifact tracing network for detection and localization of image splicing. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 375–384 (2021)

  14. Hussain, I., Tan, S., Li, B., Qin, X., Hussain, D., Huang, J.: A novel deep learning framework for double jpeg compression detection of small size blocks. J. Vis. Commun. Image Represent. 80, 103269 (2021)

    Article  Google Scholar 

  15. Yang, J., Xie, J., Zhu, G., Kwong, S., Shi, Y.-Q.: An effective method for detecting double jpeg compression with the same quantization matrix. IEEE Trans. Inf. Forensics Secur. 9(11), 1933–1942 (2014)

    Article  Google Scholar 

  16. Yuan, H., Ou, B., Tian, H.: Detecting double compression for jpeg images of low quality factor. J. Electron. Imaging 28(3), 033011–033011 (2019)

    Article  Google Scholar 

  17. Niu, Y., Li, X., Zhao, Y., Ni, R.: An enhanced approach for detecting double jpeg compression with the same quantization matrix. Signal Process. Image Commun. 76, 89–96 (2019)

  18. Niu, Y., Li, X., Zhao, Y., Ni, R.: Detection of double jpeg compression with the same quantization matrix via convergence analysis. IEEE Trans. Circuits Syst. Video Technol. 32(5), 3279–3290 (2021)

    Article  Google Scholar 

  19. Wang, J., Wang, H., Li, J., Luo, X., Shi, Y.-Q., Jha, S.K.: Detecting double jpeg compressed color images with the same quantization matrix in spherical coordinates. IEEE Trans. Circuits Syst. Video Technol. 30(8), 2736–2749 (2019)

    Article  Google Scholar 

  20. Wang, H., Wang, J., Luo, X., Zheng, Y., Ma, B., Sun, J., Jha, S.K.: Detecting aligned double jpeg compressed color image with same quantization matrix based on the stability of image. IEEE Trans. Circuits Syst. Video Technol. 32(6), 4065–4080 (2021)

    Article  Google Scholar 

  21. Harish, A.N., Verma, V., Khanna, N.: Double jpeg compression detection for distinguishable blocks in images compressed with same quantization matrix. In: 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6 (2020). IEEE

  22. Wang, J., Huang, W., Luo, X., Shi, Y.-Q., Jha, S.K.: Detecting non-aligned double jpeg compression based on amplitude-angle feature. ACM Trans. Multimed. Comput. Commun. Appl. 17(4), 1–18 (2021)

    Article  Google Scholar 

  23. USDA, N.: United states department of agriculture. Natural Resources Conservation Service. Plants Database. http://plants.usda.gov (accessed in 2000) (1999)

Download references

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).

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhihua Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yakun Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jinwei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Corresponding authors

Correspondence to Xiuli Chai or Zhihua Gan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation