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A novel forensic image analysis tool for discovering double JPEG compression clues

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Abstract

This paper presents a novel technique to discover double JPEG compression traces. Existing detectors only operate in a scenario that the image under investigation is explicitly available in JPEG format. Consequently, if quantization information of JPEG files is unknown, their performance dramatically degrades. Our method addresses both forensic scenarios which results in a fresh perceptual detection pipeline. We suggest a dimensionality reduction algorithm to visualize behaviors of a big database including various single and double compressed images. Based on intuitions of visualization, three bottom-up, top-down and combined top-down/bottom-up learning strategies are proposed. Our tool discriminates single compressed images from double counterparts, estimates the first quantization in double compression, and localizes tampered regions in a forgery examination. Extensive experiments on three databases demonstrate results are robust among different quality levels. F 1-measure improvement to the best state-of-the-art approach reaches up to 26.32 %. An implementation of algorithms is available upon request to fellows.

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Notes

  1. In simple language, the ergodicity principle refers to the proverb “You may know by a handful the whole sack.”.

  2. The RCID database is publicly available to fellow academic researchers. For accessing this database, please contact behrad@shahed.ac.ir.

References

  1. Baudat G, Anouar F (2000) Generalized discriminant analysis using a kernel approach. Neural Comput 12(10):2385–2404

    Article  Google Scholar 

  2. Berger A, Hill TP (2011) A basic theory of Benford’s law. Probab Surv 8:1–126

    Article  MathSciNet  MATH  Google Scholar 

  3. Bianchi T, Piva A (2012) Image forgery localization via block-grained analysis of JPEG artifacts. IEEE Trans Inf Forensics Secur 7(3):1003–1017

    Article  Google Scholar 

  4. Borenstein E, Ullman S (2008) Combined top-down/bottom-up segmentation. IEEE Trans Pattern Anal Mach Intell 30(12):2109–2125

    Article  Google Scholar 

  5. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3). article 27

  6. Cheng H, Liu Z, Yang L, Chen X (2013) Sparse representation and learning in visual recognition: theory and applications. Signal Process 93(6):1408–1425

    Article  Google Scholar 

  7. Dong L, Kong X, Wang B, You X (2011) Double compression detection based on Markov model of the first digits of DCT coefficients. In: IEEE 6Th international conference on image and graphics (ICIG), pp 234–237

  8. Duarte MF, Eldar YC (2011) Structured compressed sensing: from theory to applications. IEEE Trans Signal Process 59(9):4053–4085

    Article  MathSciNet  Google Scholar 

  9. Evans N, Bozonnet S, Wang D, Fredouille C, Troncy R (2012) A comparative study of bottom-up and top-down approaches to speaker diarization. IEEE Trans Audio Speech Lang Process 20(2):382–392

    Article  Google Scholar 

  10. Farid H (2006) Digital image ballistics from JPEG quantization. Tech. Rep. TR2006-583, Department of Computer Science Dartmouth College

  11. Farid H (2009) Image forgery detection. IEEE Signal Proc Mag 26(2):16–25

    Article  Google Scholar 

  12. Galvan F, Puglisi G, Bruna A, Battiato S (2014) First quantization matrix estimation from double compressed JPEG images. IEEE Trans Inf Forensics Secur 9 (8):1299–1310

    Article  Google Scholar 

  13. Gao J, Shi Q, Caetano TS (2012) Dimensionality reduction via compressive sensing. Pattern Recogn Lett 33(9):1163–1170

    Article  Google Scholar 

  14. He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263–1284

    Article  Google Scholar 

  15. Jolliffe IT (2002) Principal component analysis. Springer Series in Statistics The 2nd edition

  16. Kaski S, Peltonen J (2011) Dimensionality reduction for data visualization. IEEE Signal Proc Mag 28(2):100–104

    Article  Google Scholar 

  17. Levin A, Weiss Y (2009) Learning to combine bottom-up and top-down segmentation. Int J Comput Vis 81(1):105–118

    Article  Google Scholar 

  18. Li B, Shi YQ, Huang J (2008) Detecting doubly compressed JPEG images by using mode based first digit features. In: IEEE 10Th workshop on multimedia signal processing, pp 730–735

  19. Li XH, Zhao YQ, Liao M, Shih FY, Shi YQ (2012) Detection of tampered region for JPEG images by using mode-based first digit features. EURASIP Journal on Advances in Signal Processing 1–10

  20. Lin HT, Lin CJ, Weng RC (2007) A note on Platt’s probabilistic outputs for support vector machines. Mach Learn 68(3):267–276

    Article  Google Scholar 

  21. Lin WS, Tjoa SK, Zhao HV, Liu KR (2009) Digital image source coder forensics via intrinsic fingerprints. IEEE Trans Inf Forensics Secur 4(3):460–475

    Article  Google Scholar 

  22. Lin Z, He J, Tang X, Tang CK (2009) Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis. Pattern Recogn 42(11):2492–2501

    Article  MATH  Google Scholar 

  23. Liu J, Ji S, Ye J (2011) SLEP: sparse learning with efficient projections Arizona State University 2011

  24. Liu Q, Sung AH, Qiao M (2011) A method to detect JPEG-based double compression. In: The 8th international conference on advances in neural networks, lecture notes in computer science, Part II, pp 466–476

  25. Liu Q, Sung AH, Qiao M (2011) Neighboring joint density-based JPEG steganalysis. ACM Trans Intell Syst Technol (TIST) 2(2). article 16

  26. Lukáš J, Fridrich J (2003) Estimation of primary quantization matrix in double compressed JPEG images. In: Proc. Digital forensic research workshop

  27. van der Maaten L (2009) Learning a parametric embedding by preserving local structure. In: International conference on artificial intelligence and statistics, pp 384–391

  28. van der Maaten L, Hinton G (2008) Visualizing data using SNE. J Mach Learn Res 9:2579–2605

    MATH  Google Scholar 

  29. van der Maaten L, Postma EO, van den Herik HJ (2009) Dimensionality reduction: a comparative review. Tech. Rep. tiCC-TR 2009-005, Tilburg Centre for Creative Computing Tilburg University

  30. Mahalakshmi SD, Vijayalakshmi K, Priyadharsini S (2012) Digital image forgery detection and estimation by exploring basic image manipulations. Digit Investig 8(3):215–225

    Article  Google Scholar 

  31. Milani S, Tagliasacchi M, Tubaro S (2012) Discriminating multiple JPEG compression using first digit features. In: IEEE International conference on acoustics, speech and signal processing (ICASSP), pp 2253–2256

  32. Olmos A, Kingdom FAA (2004) A biologically inspired algorithm for the recovery of shading and reflectance images. Perception 33(12):1463–1473

    Article  Google Scholar 

  33. Pearson K (1901) On lines and planes of closest fit to systems of points in space. Phil Mag 2(11):559–572

    Article  MATH  Google Scholar 

  34. Peng Y, Liu B (2013) Accurate estimation of primary quantisation table with applications to tampering detection. Electron Lett 49(23):1452–1454

    Article  Google Scholar 

  35. Piva A (2013) An overview on image forensics. ISRN Signal Processing p article ID 496701

  36. Redi JA, Taktak W, Dugelay JL (2011) Digital image forensics: a booklet for beginners. Multimedia Tools Appl 51(1):133–162

    Article  Google Scholar 

  37. Sencar HT, Memon N (2009) Identification and recovery of JPEG files with missing fragments. Digit Investig 6:S88–S98

    Article  Google Scholar 

  38. Silverman BW (1986) Density estimation for statistics and data analysis. Monogr Stat Appl Probab 26:1–22

    MATH  Google Scholar 

  39. Soille P (2004) Morphological image analysis: principles and applications. Springer-verlag New York, Inc. The 2nd edition

  40. Taimori A, Behrad A (2015) A new deformable mesh model for face tracking using edge based features and novel sets of energy functions. Multimedia Tools Appl 74(23):10,735–10,759

    Article  Google Scholar 

  41. Taimori A, Razzazi F, Behrad A, Ahmadi A, Babaie-Zadeh M (2012) A proper transform for satisfying Benford’s law and its application to double JPEG image forensics. In: IEEE International symposium on signal processing and information technology (ISSPIT), pp. 000,240–000,244

  42. Taimori A, Razzazi F, Behrad A, Ahmadi A, Babaie-Zadeh M (2015) Quantization-unaware double JPEG compression detection. Journal of Mathematical Imaging and Vision. doi:10.1007/s10851-015-0602-z

  43. Tian H, Fang Y, Yao Z, Lin W, Ni R, Zhu Z (2014) Salient region detection by fusing bottom-up and top-down features extracted from a single image. IEEE Trans Image Process 23(10):4389–4398

    Article  MathSciNet  Google Scholar 

  44. Ulusoy I, Bishop CM (2005) Generative versus discriminative methods for object recognition. In: IEEE Computer society conference on computer vision and pattern recognition (CVPR), vol 2, pp 258–265

  45. Wallace GK (1992) The JPEG still picture compression standard. IEEE Trans Consum Electron 38(1):xviii–xxxiv

    Article  Google Scholar 

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Acknowledgment

The authors would like to thank S. Sabouri for her valuable comments which help us to improve the quality of this paper.

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Authors and Affiliations

  1. Young Researchers and Elite Club, Parand Branch, Islamic Azad University, Parand, Iran

    Ali Taimori

  2. Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, 14778-93855, Iran

    Farbod Razzazi

  3. Faculty of Engineering, Shahed University, Tehran, 18651-33191, Iran

    Alireza Behrad

  4. Department of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran, 14317-14191, Iran

    Ali Ahmadi

  5. Department of Electrical Engineering, Sharif University of Technology, Tehran, 14588-89694, Iran

    Massoud Babaie-Zadeh

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  1. Ali Taimori
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  2. Farbod Razzazi
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  3. Alireza Behrad
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  4. Ali Ahmadi
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  5. Massoud Babaie-Zadeh
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Correspondence to Ali Taimori.

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Taimori, A., Razzazi, F., Behrad, A. et al. A novel forensic image analysis tool for discovering double JPEG compression clues. Multimed Tools Appl 76, 7749–7783 (2017). https://doi.org/10.1007/s11042-016-3409-z

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