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Real-time detecting one specific tampering operation in multiple operator chains

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Abstract

Currently, many forensic techniques have been developed to determine the processing history of given multimedia contents. However, because of the interaction among tampering operations, there are still fundamental limits on the determination of tampering order and type. Up to now, a few works consider the cases where multiple operation types are involved in. In these cases, we not only need to consider the interplay of operation order, but also should quantify the detectability of one specific operation. In this paper, we propose an efficient information theoretical framework to solve this problem. Specially, we analyze the operation detection problem from the perspective of set partitioning and detection theory. Then, under certain detectors, we present the information framework to contrast the detected hypotheses and true hypotheses. Some constraint criterions are designed to improve the detection performance of an operation. In addition, Maximum-Likelihood Estimation (MLE) is used to obtain the best detector. Finally, a multiple chain set is examined in this paper, where three efficient detection methods have been proposed and the effectiveness of our framework has been demonstrated by simulations.

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References

  1. Stamm, M.C., Wu, M., Liu, K.J.R.: Information forensics: an overview of the first decade. IEEE Access 1, 167–200 (2013)

    Article  Google Scholar 

  2. Liao, X., Qin, Z., Ding, L.: Data embedding in digital images using critical functions. Signal Process. Image Commun. 58, 146–156 (2017)

    Article  Google Scholar 

  3. Qin, C., Ji, P., Chang, C., Dong, J.: Non-uniform watermark sharing based on optimal iterative BTC for image tampering recovery. IEEE Multimed. 25(3), 36–48 (2018)

    Article  Google Scholar 

  4. Liao, X., Yu, Y., Li, B., Li, Z.: A new payload partition strategy in color image steganpgraphy. IEEE Trans. Circuits Syst. Video Technol. https://doi.org/10.1109/TCSVT.2019.2896270 (2019)

  5. Qin, C., Chen, X., Luo, X., Zhang, X.: Perceptual image hashing via dual-cross pattern encoding and salient structure detection. Inf. Sci. 423, 284–302 (2018)

    Article  MathSciNet  Google Scholar 

  6. Popescu, A.C., Farid, H.: Exposing digital forgeries by detecting traces of resampling. IEEE Trans. Signal Process. 53(2), 758–767 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  7. Kirchner, M., Bohme, R.: Hiding traces of resampling in digital images. IEEE Trans. Inf. Forensics Secur. 3(4), 582–592 (2008)

    Article  Google Scholar 

  8. Boroumand, M., Fridrich, J.: Scalable processing history detector for JPEG images. Electron. Imaging 10, 128–137 (2017)

    Article  Google Scholar 

  9. Feng, X., Cox, I., Doerr, G.: Normalized energy density-based forensic detection of resampled images. IEEE Trans. Multimed. 14(3), 536–545 (2012)

    Article  Google Scholar 

  10. Stamm, M.C., Liu, K.J.R.: Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Transactions on Information Forensics and Security 5(3), 492–506 (2010)

    Article  Google Scholar 

  11. Singh, N., Gupta, A., Jain, R.C.: Global contrast enhancement based image forensics using statistical features. Adv. Electric. Electron. Eng. 15(3), 509–516 (2017)

    Article  Google Scholar 

  12. Chen, C., Ni, J., Huang, J.: Blind detection of median filtering in digital images: a difference domain based approach. IEEE Trans. Image Process. 22(12), 4699–4710 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  13. Kang, X., Stamm, M.C., Peng, A., Liu, K.J.R.: Robust median filtering forensics using an autoregressive model. IEEE Trans. Inf. Forensics Secur. 8(9), 1456–1468 (2013)

    Article  Google Scholar 

  14. Su, B., Lu, S., Tan, C.L.: Blurred image region detection and classification. In: ACM International Conference on Multimedia, pp. 1397–1400 (2011)

  15. Li, J., Li, X., Yang, B., Sun, X.: Segmentation-based image copy-move forgery detection scheme. IEEE Trans. Inf. Forensics Secur. 10(3), 507–518 (2015)

    Article  Google Scholar 

  16. Cai, K., Lu, X., Song, J., Wang, X.: Blind image tampering identification based on histogram features. In: International Conference on Multimedia Information Networking and Security (MINES), pp. 300–303 (2011)

  17. Li, H., Luo, W., Qiu, X.: Identification of various image operations using residual-based features. IEEE Trans. Circuits Syst. Video Technol. 28(1), 31–45 (2018)

    Article  Google Scholar 

  18. Qiu, X., Li, H., Luo, W.: A universal image forensic strategy based on steganalytic model. In: ACM workshop on Information hiding and multimedia security, pp. 165–170 (2014)

  19. Cao, G., Zhao, Y., Ni, R.: Contrast enhancement-based forensics in digital images. IEEE Trans. Inf. Forensics Secur. 9(3), 515–525 (2014)

    Article  Google Scholar 

  20. Chen, Z., Zhao, Y., Ni, R.: Detection of operation chain: JPEG-resampling-JPEG. Signal Process. Image Commun. 57, 8–20 (2017)

    Article  Google Scholar 

  21. Comesa\(\tilde{n}\)a, P.: Detection and information theoretic measures for quantifying the distinguishability between multimedia operator chains. In: IEEE International Workshop on Information Forensics and Security, pp. 211–216 (2012)

  22. Stamm, M. C., Chu, X., Liu, K. J. R.: Forensically determining the order of signal processing operations. In: IEEE International Workshop on Information Forensics and Security, pp. 162–167 (2013)

  23. Liu, Y., Zhao, Y., Ni, R.: Forensics of image blurring and sharpening history based on NSCT domain. In: Signal and Information Processing Association Annual Summit and Conference, pp. 1–4 (2014)

  24. Chu, X., Chen, Y., Liu, K. R.: An information theoretic framework for order of operations forensics. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2049–2053 (2016)

  25. Gao, S., Liao, X., Guo, S.: Forensic detection for image operation order: resizing and contrast Enhancement. In: International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, pp. 570–580 (2017)

  26. Li, J., Liao, X., Hu, R., Liu, X.: Detectability of the image operation order: upsampling and mean filtering. In: IEEE Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 1544–1549 (2018)

  27. Patrick, B., Filler, T., Pevn\(\acute{y}\), T.: Break our steganographic system: the ins and outs of organizing BOSS. In: International Workshop on Information Hiding, pp. 59–70 (2011)

  28. Schaefer, G., Stich, M.: UCID: an uncompressed color image database. Storage Retr. Methods Appl. Multimed. 5307(1), 472–481 (2004)

    Google Scholar 

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Acknowledgements

This work is supported by National Natural Science Foundation of China (Grant nos. 61402162, 61772191), Hunan Provincial Natural Science Foundation (Grant no. 2017JJ3040), Open Project Program of National Laboratory of Pattern Recognition (Grant no. 201900017), Open Research Fund of Key Laboratory of Network Crime Investigation of Hunan Provincial Colleges (Grant no. 2017WL-FZZC001), the Key Lab of Information Network Security and the Ministry of Public Security of China (Grant no. C17606), and CERNET Innovation Project (Grant no. NGII20180412).

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Shangde Gao & Xin Liao

  2. Key Laboratory of Network Crime Investigation of Hunan Provincial Colleges, Changsha, 410138, China

    Xin Liao & Xuchong Liu

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  1. Shangde Gao
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  2. Xin Liao
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  3. Xuchong Liu
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Correspondence to Xin Liao.

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Gao, S., Liao, X. & Liu, X. Real-time detecting one specific tampering operation in multiple operator chains. J Real-Time Image Proc 16, 741–750 (2019). https://doi.org/10.1007/s11554-019-00860-3

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