Skip to main content
Springer Nature Link
Log in

Comparative Compression Robustness Evaluation of Digital Image Forensics

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2022 (ICCSA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13376))

Included in the following conference series:

  • 703 Accesses

Abstract

The robustness of two important digital image forensic tasks (i.e. SIFT-based copy-move forgery detection and PRNU-based camera sensor identification) against four different lossy compression techniques is investigated (while typically, only JPEG compression is considered) to identify the best suited technique for this application scenario. Overall, we find that the accuracy of forensic tasks is reduced for increasing compression strength as expected, however, the relative performance of the compression schemes is different for the two tasks. While JPEG is superior for realistic application settings (where accuracy is in an acceptable range) in SIFT-based copy-move forgery detection, JPEG XR and BPG provide the best option for PRNU-based camera sensor identification, whereas JPEG is clearly impacting this forensic application most severely.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://bellard.org/bpg/.

  2. 2.

    http://opencv.org/.

  3. 3.

    https://dde.binghamton.edu/download/camera_fingerprint/.

References

  1. Sencar, H.T., Memon, N.: Digital Image Forensics: There is More To a Picture than Meets the Eye. Springer Verlag, New York (2012). https://doi.org/10.1007/978-1-4614-0757-7

    Book  Google Scholar 

  2. Ardizzone, E., Bruno, A., Mazzola, G.: Detecting multiple copies in tampered images. In: 2010 IEEE International Conference on Image Processing, pp. 2117–2120. IEEE (2010)

    Google Scholar 

  3. Christlein, V., Riess, C., Jordan, J., Riess, C., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012)

    Article  Google Scholar 

  4. Zandi, M., Mahmoudi-Aznaveh, A., Mansouri, A.: Adaptive matching for copy-move forgery detection. In: 2014 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 119–124. IEEE (2014)

    Google Scholar 

  5. Mushtaq, S., Mir, A.H.: Image copy move forgery detection: a review. Int. J. Future Gener. Commun. Netw. 11(2), 11–22 (2018)

    Article  Google Scholar 

  6. Abdalla, Y., Iqbal, M.T., Shehata, M.: Copy-move forgery detection and localization using a generative adversarial network and convolutional neural-network. Information 10, 286 (2019)

    Article  Google Scholar 

  7. Wu, Y., Abd-Almageed, W., Natarajan, P.: BusterNet: detecting copy-move image forgery with source/target localization. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 170–186. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_11

    Chapter  Google Scholar 

  8. Huang, H.-Y., Ciou, A.-J.: Copy-move forgery detection for image forensics using the superpixel segmentation and the Helmert transformation. EURASIP J. Image Video Process. 2019(1), 1–16 (2019). https://doi.org/10.1186/s13640-019-0469-9

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Bilal, M., Habib, H.A., Mehmood, Z., Yousaf, R.M., Saba, T., Rehman, A.: A robust technique for copy-move forgery detection from small and extremely smooth tampered regions based on the DHE-SURF features and mDBSCAN clustering. Australian J. Forensic Sci. 53, 1–24 (2020)

    Google Scholar 

  11. Saleem, M.: A key-point based robust algorithm for detecting cloning forgery. In: IEEE International Conference on Control System, Computing and Engineering (ICCSCE), vol. 4, pp. 2775–2779 (2014)

    Google Scholar 

  12. Do, T.T., Kijak, E., Furon, T., Amsaleg, L.: Deluding image recognition in SIFT-based CBIR systems. In: Proceedings of the 2nd ACM Workshop on Multimedia in Forensics, Security and Intelligence, pp. 7–12. ACM (2010)

    Google Scholar 

  13. Chierchia, G., Poggi, G., Sansone, C., Verdoliva, L.: PRNU-based forgery detection with regularity constraints and global optimization. In: 2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP), pp.236–241 (2013)

    Google Scholar 

  14. Lukas, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1(2), 205–214 (2006)

    Article  Google Scholar 

  15. Fridrich, J.: Digital image forensics. IEEE Signal Process. Mag. 26(2), 26–37 (2009)

    Article  Google Scholar 

  16. Huang, D.Y., Huang, C.N., Hu, W.C., Chou, C.H.: Robustness of copy-move forgery detection under high jpeg compression artifacts. Multimed. Tools Appl. 76, 1509–1530 (2015)

    Article  Google Scholar 

  17. Huang, D.Y., Huang, C.N., Hu, W.C., Chou, C.H.: Robustness of copy-move forgery detection under high jpeg compression artifacts. Multimedia Tools Appl. 76(1), 1509–1530 (2017)

    Article  Google Scholar 

  18. Joechl, R., Uhl, A.: Effects of image compression on image age approximation. In: 20th International Workshop on Digital-forensics and Watermarking (IWDW2021), Beijing, China (2021)

    Google Scholar 

  19. Goljan, M., Chen, M., Comesara, P., Fridrich, J.: Effect of compression on sensor-fingerprint based camera identification. Electron. Imaging. Media Watermark. Secur. Forensics 2016, 1–10 (2016)

    Google Scholar 

  20. Kumawat, C., Pankajakshan, V.: A robust jpeg compression detector for image forensics. Signal Process. Image Commun. 89, 116008 (2020)

    Article  Google Scholar 

  21. Stamm, M.C., Liu, K.R.: Anti-forensics of digital image compression. IEEE Trans. Inf. Forensics Secur. 6(3), 1050–1065 (2011)

    Article  Google Scholar 

  22. Stamm, M.C., Tjoa, S.K., Lin, W.S., Liu, K.R.: Undetectable image tampering through jpeg compression anti-forensics. In: 2010 IEEE International Conference on Image Processing, pp. 2109–2112 (2010)

    Google Scholar 

  23. Lu, W., Zhang, Q., Luo, S., Zhou, Y., Huang, J., Shi, Y.Q.: Robust estimation of upscaling factor on double jpeg compressed images. IEEE Trans. Cybern. 1–13 (2021)

    Google Scholar 

  24. Diallo, B., Urruty, T., Bourdon, P., Fernandez-Maloigne, C.: Robust forgery detection for compressed images using CNN supervision. Forensic Sci. Int. Rep. 2, 100112 (2020)

    Article  Google Scholar 

  25. Diallo, B., Urruty, T., Bourdon, P., Fernandez-Maloigne, C.: Improving robustness of image tampering detection for compression. In: MMM 2019: MultiMedia Modeling, Thessaloniki, Greece, January 2019

    Google Scholar 

  26. Mandelli, S., Bonettini, N., Bestagini, P., Tubaro, S.: Training CNNs in presence of jpeg compression: Multimedia forensics vs computer vision. In: 2020 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6 (2020)

    Google Scholar 

  27. Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Serra, G.: A sift-based forensic method for copy-move attack detection and transformation recovery. IEEE Trans. Inf. Forensics Secur. 6(3), 1099–1110 (2011)

    Article  Google Scholar 

  28. Mahdian, B., Saic, S.: Detection of copy-move forgery using a method based on blur moment invariants. Forensic Sci. Int. 171(2–3), 180–189 (2007)

    Article  Google Scholar 

  29. Huang, H., Guo, W., Zhang, Y.: Detection of copy-move forgery in digital images using SIFT algorithm. In: Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008. PACIIA 2008, vol. 2, pp. 272–276. IEEE (2008)

    Google Scholar 

  30. Mihcak, M.K., Kozintsev, I., Ramchandran, K.: Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising. In: Proceedings of 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1999, vol. 6, pp. 3253–3256. IEEE (1999)

    Google Scholar 

  31. Pennebaker, W., Mitchell, J.: JPEG - Still Image Compression Standard. Van Nostrand Reinhold, New York (1993)

    Google Scholar 

  32. Taubman, D., Marcellin, M.: JPEG2000 – Image Compression Fundamentals. Standards and Practice. Kluwer Academic Publishers, The Nethrlands (2002)

    Book  Google Scholar 

  33. Dufaux, F., Sullivan, G.J., Ebrahimi, T.: The JPEG XR image coding standard. IEEE Signal Process. Mag. 26(6), 195–199 (2009)

    Article  Google Scholar 

  34. Li, F., Krivenko, S., Lukin, V.: An approach to better portable graphics (BPG) compression with providing a desired quality. In: 2020 IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT), pp. 13–17 (2020)

    Google Scholar 

  35. Liu, F., Hernandez-Cabronero, M., Sanchez, V., Marcellin, M.W., Bilgin, A.: The current role of image compression standards in medical imaging. Information 8(4), 131 (2017)

    Article  Google Scholar 

  36. Hofbauer, H., Rathgeb, C., Wagner, J., Uhl, A., Busch, C.: Investigation of better portable graphics compression for iris biometric recognition. In: Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG 2015), Darmstadt, Germany, p. 8 (2015)

    Google Scholar 

  37. Rathgeb, C., Busch, C., Wagner, J., Pflug, A.: Effects of image compression on ear biometrics. IET Biom. 5(3), 252–261 (2016)

    Article  Google Scholar 

  38. Darwiche, M., Pham, T.A., Delalandre, M.: Comparison of jpeg’s competitors for document images. In: 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 487–493 (2015)

    Google Scholar 

  39. Wild, P., Štolc, S., Valentín, K., Daubner, F., Clabian, M.: Compression effects on security document images. In: 2016 European Intelligence and Security Informatics Conference (EISIC), pp. 76–79 (2016)

    Google Scholar 

  40. Ardizzone, E., Bruno, A., Mazzola, G.: Copy-move forgery detection by matching triangles of keypoints. IEEE Trans. Inf. Forensics Secur. 10(10), 2084–2094 (2015)

    Article  Google Scholar 

  41. Gloe, T., Böhme, R.: The Dresden image database for benchmarking digital image forensics. In: SAC 2010: Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1584–1590ACM (2010)

    Google Scholar 

Download references

Acknowledgments

The authors gratefully acknowlege the contribution of the further Media Data Formats Lab students Max Göttl, Christoph Schwengler, Dennis Strähhuber, Benedikt Streitwieser, and Tim Ungerhofer. This work has been partially supported by the Salzburg State Government project “Artificial Intelligence in Industrial Vision Salzburg”.

Author information

Authors and Affiliations

  1. Visual Computing and Security Lab (VISEL), Department of Artificial Intelligence and Human Interfaces, University of Salzburg, Salzburg, Austria

    Oliver Remy, Sebastian Strumegger, Jutta Hämmerle-Uhl & Andreas Uhl

Authors
  1. Oliver Remy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sebastian Strumegger
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jutta Hämmerle-Uhl
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andreas Uhl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andreas Uhl .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Universidad de Málaga, Malaga, Spain

    Eligius M. T. Hendrix

  4. Monash University, Clayton, VIC, Australia

    David Taniar

  5. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Remy, O., Strumegger, S., Hämmerle-Uhl, J., Uhl, A. (2022). Comparative Compression Robustness Evaluation of Digital Image Forensics. In: Gervasi, O., Murgante, B., Hendrix, E.M.T., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2022. ICCSA 2022. Lecture Notes in Computer Science, vol 13376. Springer, Cham. https://doi.org/10.1007/978-3-031-10450-3_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-10450-3_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10449-7

  • Online ISBN: 978-3-031-10450-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics