Skip to main content
SpringerLink
Log in

Analysis of large-deviation multifractal spectral properties through successive compression for double JPEG detection

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Modern commercial software tools have the ability to deceive a viewer who is unable to determine whether the image content is authentic or not. Research on visual traces, image modifications as attacks, and possible misleading forensic analysis in practice, led to reexamining common used formats, like JPEG (Joint Photographic Experts Group) compressed images. This is one of the most popular image and media formats on the Internet that convey information that cannot be easily trusted. Recompression is one of the fundamental aspects to be investigated, where double JPEG (DJPEG) compression is analyzed through spectral and statistical properties. State-of-the-art methods use coefficients to employ characteristics, like periodicity in histogram spectra for various quality factors (QFs). Some of the studies consider only DJPEG estimations when primary QF is less than in a latter case or when the same quantization matrix is applied. In this paper DJPEG and SJPEG (single JPEG) images are considered through large-deviation spectrum method (LDSM) and rounding and truncating (RT) errors, where additional two successive compressions are employed. The proposed methodology gives promising way to address classification between SJPEG and DJPEG. The test results are obtained on publically available image sets and show the effectiveness of the proposed approach with low number of features compared to other available methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

The datasets analyzed during the current study are/have been available at https://qualinet.github.io/databases/image/uncompressed_colour_image_database_ucid/, https://www.flickr.com/photos/usdagov/collections/72157624326158670/, http://loki.disi.unitn.it/RAISE/, and/or necessary information can be found in ref. [10, 38, 44]. The Lena image can be downloaded from: https://www.imageprocessingplace.com/root_files_V3/image_databases.htm

References

  1. Barral J, Gonçalves P (2011) On the estimation of the large deviations Spectrum. J Stat Phys 144(6):1256–1283. https://doi.org/10.1007/s10955-011-0296-6

    Article  MathSciNet  MATH  Google Scholar 

  2. Bhartiya G, Jalal AS (2017) Forgery detection using feature-clustering in recompressed JPEG images. Multimed Tools Appl 76(20):20799–20814. https://doi.org/10.1007/s11042-016-3964-3

    Article  Google Scholar 

  3. Bianchi T, Piva A (2011) Detection of nonaligned double JPEG compression based on integer periodicity maps. IEEE Trans Inf Forensics Secur 7(2):842–848. https://doi.org/10.1109/TIFS.2011.2170836

    Article  Google Scholar 

  4. Bianchi T, De Rosa A, Piva A (2011, May) Improved DCT coefficient analysis for forgery localization in JPEG images. In 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp 2444-2447). IEEE. https://doi.org/10.1109/ICASSP.2011.5946978

  5. Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: a survey. Digit Investig 10(3):226–245. https://doi.org/10.1016/j.diin.2013.04.007

    Article  Google Scholar 

  6. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140. https://doi.org/10.1007/BF00058655

    Article  MATH  Google Scholar 

  7. Chen YL, Hsu CT (2011) Detecting recompression of JPEG images via periodicity analysis of compression artifacts for tampering detection. IEEE Trans Inf Forensics Secur 6(2):396–406. https://doi.org/10.1109/TIFS.2011.2106121

    Article  Google Scholar 

  8. Chouhan A, Nigam MJ (2016, July) Double compression of JPEG image using DCT with estimated quality factor. In 2016 IEEE 1st international conference on power electronics, intelligent control and energy systems (ICPEICES) (pp 1-3). IEEE. https://doi.org/10.1109/ICPEICES.2016.7853478

  9. Dalmia N, Okade M (2018) Robust first quantization matrix estimation based on filtering of recompression artifacts for non-aligned double compressed JPEG images. Signal Process Image Commun 61:9–20. https://doi.org/10.1016/j.image.2017.10.011

    Article  Google Scholar 

  10. Dang-Nguyen DT, Pasquini C, Conotter V, Boato G (2015, March) Raise: a raw images dataset for digital image forensics. In Proceedings of the 6th ACM multimedia systems conference (pp 219-224). https://doi.org/10.1145/2713168.2713194

  11. Fan Z, De Queiroz RL (2003) Identification of bitmap compression history: JPEG detection and quantizer estimation. IEEE Trans Image Process 12(2):230–235. https://doi.org/10.1109/TIP.2002.807361

    Article  Google Scholar 

  12. Farid H (2009) Image forgery detection. IEEE Signal Process Mag 26(2):16–25. https://doi.org/10.1109/MSP.2008.931079

    Article  Google Scholar 

  13. Farid H (2009) Exposing digital forgeries from JPEG ghosts. IEEE Trans Inf Forensics Secur 4(1):154–160. https://doi.org/10.1109/TIFS.2008.2012215

    Article  MathSciNet  Google Scholar 

  14. Fatimah B, Singh P, Singhal A, Pachori RB (2021) Hand movement recognition from sEMG signals using Fourier decomposition method. Biocybern Biomed Eng 41(2):690–703. https://doi.org/10.1016/j.bbe.2021.03.004

    Article  Google Scholar 

  15. Feng X, Doërr G (2010, January) JPEG recompression detection. In Media forensics and security II, vol. 7541 pp 75410J. International Society for Optics and Photonics. https://doi.org/10.1117/12.838888

  16. Fraclab (n.d.) https://project.inria.fr/fraclab/, last accessed (30.08.2021.)

  17. Fu D, Shi YQ, Su W (2007) A generalized Benford’s law for JPEG coefficients and its applications in image forensics. In Security, steganography, and watermarking of multimedia contents IX vol. 6505 pp 65051L. International society for optics and photonics. https://doi.org/10.1117/12.704723

  18. Galvan F, Puglisi G, Bruna AR, Battiato S (2013, September) First quantization coefficient extraction from double compressed JPEG images. In International conference on image analysis and processing (pp 783-792). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41181-6_79

  19. Gavrovska A, Zajic G, Reljin I, Reljin B (2013) Classification of prolapsed mitral valve versus healthy heart from phonocardiograms by multifractal analysis. Comput Math Methods Med 2013:1–10. https://doi.org/10.1155/2013/376152

    Article  MathSciNet  Google Scholar 

  20. Halim Z (2018) Optimizing the minimum spanning tree-based extracted clusters using evolution strategy. Clust Comput 21(1):377–391. https://doi.org/10.1007/s10586-017-0868-6

    Article  MathSciNet  Google Scholar 

  21. He J, Lin Z, Wang L, Tang X (2006, May) Detecting doctored JPEG images via DCT coefficient analysis. In European conference on computer vision (pp 423-435). Springer, Berlin, Heidelberg. https://doi.org/10.1007/11744078_33

  22. Hou W, Ji Z, Jin X, Li X (2013) Double JPEG compression detection based on extended first digit features of DCT coefficients. Int J Inf Educ Technol 3(5):512

    Google Scholar 

  23. Huang F, Huang J, Shi YQ (2010) Detecting double JPEG compression with the same quantization matrix. IEEE Trans Inf Forensics Secur 5(4):848–856. https://doi.org/10.1109/TIFS.2010.2072921

    Article  Google Scholar 

  24. Ihlen EA, Vereijken B (2013) Multifractal formalisms of human behavior. Hum Mov Sci 32(4):633–651. https://doi.org/10.1016/j.humov.2013.01.008

    Article  Google Scholar 

  25. JPEG Toolbox (n.d.) https://digitnet.github.io/jpeg-toolbox/, last accessed (15.07.2021)

  26. Karaca BK, Akşahin MF, Öcal R (2021) Detection of multiple sclerosis from photic stimulation EEG signals. Biomed Signal Process Control 67:102571. https://doi.org/10.1016/j.bspc.2021.102571

    Article  Google Scholar 

  27. Li B, Shi YQ, Huang J (2008, October) Detecting doubly compressed JPEG images by using mode based first digit features. In 2008 IEEE 10th workshop on multimedia signal processing (pp 730-735). IEEE. https://doi.org/10.1109/MMSP.2008.4665171

  28. Li B, Luo H, Zhang H, Tan S, Ji Z (2017) A multi-branch convolutional neural network for detecting double JPEG compression. arXiv preprint arXiv:1710.05477

  29. 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. https://doi.org/10.1016/j.patcog.2009.03.019

    Article  MATH  Google Scholar 

  30. Lukáš J, Fridrich J (2003, August) Estimation of primary quantization matrix in double compressed JPEG images. In Proc. digital forensic research workshop (pp 5-8)

  31. Luo W, Huang J, Qiu G (2010) JPEG error analysis and its applications to digital image forensics. IEEE Trans Inf Forensics Secur 5(3):480–491. https://doi.org/10.1109/TIFS.2010.2051426

    Article  Google Scholar 

  32. Mandelbrot BB (1982) The fractal geometry of nature, vol 1. WH freeman, New York. https://doi.org/10.1002/esp.3290080415

    Book  MATH  Google Scholar 

  33. Meena KB, Tyagi V (2019) Image forgery detection: survey and future directions. In: Data, engineering and applications. Springer, Singapore, pp 163–194. https://doi.org/10.1007/978-981-13-6351-1_14

    Chapter  Google Scholar 

  34. Mitchell JL, Pennebaker WB (1991) Evolving JPEG color data compression standard. In: Standards for electronic imaging systems: a critical review, vol 10259. International Society for Optics and Photonics, p 1025906. https://doi.org/10.1117/12.48892

    Chapter  Google Scholar 

  35. Nawayi SH, Vijean V, Salleh AF, Planiappan R, Lim CC, Fook CY, Awang AS (2021, October) Non-invasive detection of Ketum users through objective analysis of EEG signals. J Phys Conf Ser 2071(1):012045). IOP publishing. https://doi.org/10.1088/1742-6596/2071/1/012045

  36. Niu Y, Li X, Zhao Y, Ni R (2019) An enhanced approach for detecting double JPEG compression with the same quantization matrix. Signal Process Image Commun 76:89–96. https://doi.org/10.1016/j.image.2019.04.016

    Article  Google Scholar 

  37. Niu Y, Tondi B, Zhao Y, Barni M (2019) Primary quantization matrix estimation of double compressed JPEG images via CNN. IEEE Signal Process Lett 27:191–195. https://doi.org/10.1109/LSP.2019.2962997

    Article  Google Scholar 

  38. NRCS Photo gallery, (n.d.) https://photogallery.sc.egov.usda.gov/photogallery/#/, last accessed (15.07.2021.)

  39. Park J, Cho D, Ahn W, Lee HK (2018) Double JPEG detection in mixed JPEG quality factors using deep convolutional neural network. In Proceedings of the European conference on computer vision (ECCV) (pp 636-652). https://doi.org/10.1007/978-3-030-01228-1_39

  40. Pasquini C, Schöttle P, Böhme R, Boato G, Perez-Gonzalez F (2016, June) Forensics of high quality and nearly identical jpeg image recompression. In Proceedings of the 4th ACM workshop on information hiding and multimedia security (pp 11-21). https://doi.org/10.1145/2909827.2930787

  41. Pavlović A, Glišović N, Gavrovska A, Reljin I (2019) Copy-move forgery detection based on multifractals. Multimed Tools Appl 78(15):20655–20678. https://doi.org/10.1007/s11042-019-7277-1

    Article  Google Scholar 

  42. Peng F, Zhou DL, Long M, Sun XM (2017) Discrimination of natural images and computer generated graphics based on multi-fractal and regression analysis. AEU-Int J Electron Commun 71:72–81. https://doi.org/10.1016/j.aeue.2016.11.009

    Article  Google Scholar 

  43. Peyrière J (1992) Multifractal measures. In Probabilistic and stochastic methods in analysis, with applications (pp 175–186). Springer, Dordrecht. https://doi.org/10.1007/978-94-011-2791-2_7

  44. Schaefer G, Stich M (2004) UCID - An Uncompressed Colour Image Database, Proc. SPIE, Storage and Retrieval Methods and Applications for Multimedia 2004, pp 472–480, San Jose, USA. https://doi.org/10.1117/12.525375

  45. Shutaywi M, Kachouie NN (2021) Silhouette analysis for performance evaluation in machine learning with applications to clustering. Entropy 23(6):759. https://doi.org/10.3390/e23060759

    Article  MathSciNet  Google Scholar 

  46. Stamm MC, Tjoa SK, Lin WS, Liu KR (2010, March) Anti-forensics of JPEG compression. In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing (pp 1694-1697). IEEE. https://doi.org/10.1109/ICASSP.2010.5495491

  47. Tondi B, Costanzo A, Huang D, Li B (2021) Boosting CNN-based primary quantization matrix estimation of double JPEG images via a classification-like architecture. EURASIP J Inf Secur 2021(1):1–14. https://doi.org/10.1186/s13635-021-00119-0

    Article  Google Scholar 

  48. Valenzise G, Tagliasacchi M, Tubaro S (2012) Revealing the traces of JPEG compression anti-forensics. IEEE Trans Inf Forensics Secur 8(2):335–349. https://doi.org/10.1109/TIFS.2012.2234117

    Article  Google Scholar 

  49. Véhel JL, Rams M (2012) Large deviation multifractal analysis of a class of additive processes with correlated nonstationary increments. IEEE/ACM Trans Networking 21(4):1309–1321

    Article  Google Scholar 

  50. Véhel JL, Tricot C (2004) On various multifractal spectra. In Fractal geometry and stochastics III (pp 23–42). Birkhäuser, Basel

  51. Wallace GK (1992) The JPEG still picture compression standard. IEEE Trans Consum Electron 38(1):xviii–xxxiv. https://doi.org/10.1109/30.125072

    Article  Google Scholar 

  52. Wang Q, Zhang R (2016) Double JPEG compression forensics based on a convolutional neural network. EURASIP J Inf Secur 2016:23. https://doi.org/10.1186/s13635-016-0047-y

  53. Wang X, Zhang D, Guo X (2013) Novel hybrid fractal image encoding algorithm using standard deviation and DCT coefficients. Nonlinear Dyn 73(1-2):347–355. https://doi.org/10.1007/s11071-013-0790-2

  54. Wei W, Rang-ding WANG (2012) The analysis and detection of double JPEG2000 compression based on statistical characterization of DWT coefficients. Energy Procedia 17:623–629. https://doi.org/10.1016/j.egypro.2012.02.145

    Article  Google Scholar 

  55. Wiseman Y (2015) The still image lossy compression standard-JPEG. In: Encyclopedia of information science and technology, 3rd edn. IGI global, pp 295–305. https://doi.org/10.4018/978-1-4666-5888-2.ch028

    Chapter  Google Scholar 

  56. Yang J, Zhu G, Wang J, Shi YQ (2013, October) Detecting non-aligned double JPEG compression based on refined intensity difference and calibration. In International Workshop on Digital Watermarking (pp 169-179). Springer, Berlin, Heidelberg. https://doi.org/10.1145/3464388

  57. Yang J, Xie J, Zhu G, Kwong S, Shi YQ (2014) An effective method for detecting double JPEG compression with the same quantization matrix. IEEE Trans Inf Forensics Secur 9(11):1933–1942. https://doi.org/10.1109/TIFS.2014.2359368

    Article  Google Scholar 

  58. Yao H, Wei H, Qin C, Zhang X (2020) An improved first quantization matrix estimation for nonaligned double compressed JPEG images. Signal Process 170:107430. https://doi.org/10.1016/j.sigpro.2019.107430

    Article  Google Scholar 

  59. Yao H, Wei H, Qiao T, Qin C (2020) JPEG quantization step estimation with coefficient histogram and spectrum analyses. J Vis Commun Image Represent 69:102795. https://doi.org/10.1016/j.jvcir.2020.102795

    Article  Google Scholar 

  60. Ye S, Sun Q, Chang EC (2007, July) Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In 2007 IEEE international conference on multimedia and expo (pp 12-15). IEEE. https://doi.org/10.1109/ICME.2007.4284574

  61. Yuan H, Ou B, Tian H (2019) Detecting double compression for JPEG images of low quality factor. J Electron Imaging 28(3):033011. https://doi.org/10.1117/1.JEI.28.3.033011

    Article  Google Scholar 

  62. Zach F, Riess C, Angelopoulou E (2012, August) Automated image forgery detection through classification of JPEG ghosts. In Joint DAGM (German Association for Pattern Recognition) and OAGM symposium (pp 185-194). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32717-9_19

  63. Zhang Y, Song W, Wu F, Han H, Zhang L (2020) Revealing the traces of nonaligned double JPEG compression in digital images. Optik 204:164196. https://doi.org/10.1016/j.ijleo.2020.164196

    Article  Google Scholar 

  64. Zhu N, Shen J, Niu X (2019) Double JPEG compression detection based on noise-free DCT coefficients mixture histogram model. Symmetry 11(9):1119. https://doi.org/10.3390/sym11091119

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Belgrade - School of Electrical Engineering, Bulevar Kralja Aleksandra 73, Belgrade, 11120, Serbia

    Ana Gavrovska

Authors
  1. Ana Gavrovska
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ana Gavrovska.

Ethics declarations

Conflict of interests

The author has no conflict of interest to declare.

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

Gavrovska, A. Analysis of large-deviation multifractal spectral properties through successive compression for double JPEG detection. Multimed Tools Appl 82, 36255–36277 (2023). https://doi.org/10.1007/s11042-023-15130-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15130-5

Keywords

Search

Navigation