Information and Communication Technology - EurAsia Conference

ICT-EurAsia 2014: Information and Communication Technology pp 643-652 | Cite as

Experimental Evaluation of an Algorithm for the Detection of Tampered JPEG Images

  • Giuseppe Cattaneo
  • Gianluca Roscigno
  • Umberto Ferraro Petrillo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8407)


This paper aims to experimentally evaluate the performance of one popular algorithm for the detection of tampered JPEG images: the algorithm by Lin et al. [1]. We developed a reference implementation for this algorithm and performed a deep experimental analysis, by measuring its performance when applied to the images of the CASIA TIDE public dataset, the de facto standard for the experimental analysis of this family of algorithms. Our first results were very positive, thus confirming the good performance of this algorithm. However, a closer inspection revealed the existence of an unexpected anomaly in a consistent part of the images of the CASIA TIDE dataset that may have influenced our results as well as the results of previous studies conducted using this dataset. By taking advantage of this anomaly, we were able to develop a variant of the original algorithm which exhibited better performance on the same dataset.


Digital Image Forensics JPEG Image Integrity Double Quantization Effect Evaluation Datasets 


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  1. 1.
    Lin, Z., He, J., Tang, X., Tang, C.-K.: Fast, Automatic and Fine-grained Tampered JPEG Image Detection via DCT Coefficient Analysis. Pattern Recognition 42(11), 2492–2501 (2009)CrossRefMATHGoogle Scholar
  2. 2.
    Lukáš, J., Fridrich, J., Goljan, M.: Digital Camera Identification from Sensor Pattern Noise. IEEE Transactions on Information Forensics and Security 1, 205–214 (2006)CrossRefGoogle Scholar
  3. 3.
    Khanna, N., Mikkilineni, A.K., Chiu, G.T.C., Allebach, J.P., Delp, E.J.: Scanner Identification using Sensor Pattern Noise. In: Proceedings of the SPIE International Conference on Security, Steganography, and Watermarking of Multimedia Contents IX, vol. 6505(1), pp. 1–11 (2007)Google Scholar
  4. 4.
    Ye, S., Sun, Q., Chang, E.-C.: Detecting Digital Image Forgeries by Measuring Inconsistencies of Blocking Artifact. In: IEEE International Conference on Multimedia and Expo 2007, pp. 12–15 (2007)Google Scholar
  5. 5.
    Farid, H.: Exposing Digital Forgeries from JPEG Ghosts. IEEE Transactions on Information Forensics and Security 4(1), 154–160 (2009)CrossRefGoogle Scholar
  6. 6.
    Cattaneo, G., Faruolo, P., Petrillo, U.F.: Experiments on improving sensor pattern noise extraction for source camera identification. In: 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 609–616 (July 2012)Google Scholar
  7. 7.
    Castiglione, A., Cattaneo, G., Cembalo, M., Petrillo, U.F.: Experimentations with Source Camera Identification and Online Social Networks. Journal of Ambient Intelligence and Humanized Computing 4(2), 265–274 (2013)CrossRefGoogle Scholar
  8. 8.
    Institute of Automation, Chinese Academy of Sciences (CASIA). CASIA Tampered Image Detection Evaluation Database (CASIA TIDE) v2.0 (2013),
  9. 9.
    Wallace, G.K.: The JPEG Still Picture Compression Standard. Communications of the ACM, 30–44 (1991)Google Scholar
  10. 10.
    Independent JPEG Group code library (December 2013),
  11. 11.
    Lukáš, J., Fridrich, J.: Estimation of Primary Quantization Matrix in Double Compressed JPEG Images. In: Proc. Digital Forensic Research Workshop, pp. 5–8 (2003)Google Scholar
  12. 12.
    Tom Lane and the Independent JPEG Group (IJG). libjpeg (2013),
  13. 13.
    Abeel, T., de Peer, Y.V., Saeys, Y.: Java-ML: A Machine Learning Library. Journal of Machine Learning Research 10, 931–934 (2009)MathSciNetMATHGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Giuseppe Cattaneo
    • 1
  • Gianluca Roscigno
    • 1
  • Umberto Ferraro Petrillo
    • 2
  1. 1.Dipartimento di InformaticaUniversità degli Studi di SalernoFiscianoItaly
  2. 2.Dipartimento di Scienze StatisticheUniversità di Roma “La Sapienza”RomaItaly

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