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Reviewing Image Data: Detecting Forgery Using a Robust Forensic Method

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Abstract

Over the past two decades, the Internet or web components, simply becoming acquainted with modern digital technology, have made the world more connected than ever before in the digital environment of everyday life, e.g., for documentation and communication of events and multimedia data transfer. On the other hand, hackers and attackers see the current situation of data transfer as an opportunity to exploit an individual's or organization’s data. Digital images used on social media are among the data that attract hackers for exploitation, trade, and other forms of digital crime. Due to the availability of many tools, it is simple to copying and editing the original image, leaving no trace. The problem of forged image, which has been more frequent in the last decade, is being addressed by an emerging field of research known as digital image forensics. In this study, a systemic review and current approach for addressing this issue has been presented.

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References

  1. Mane VA, Vanita. Reflection SIFT for improving the detection of copy–move image forgery. In: ICRCICN, 2016; Vol. 1, pp. 84–88.

  2. Warbhe A, Dharaskar R, Thakare V. Block based image forgery detection techniques. Int J Eng Sci Res Technol. 2015;4(8):289–97.

    Google Scholar 

  3. Bitajdar GK, Mankar VH. Digital image forgery detection using passive techniques: a survey. Digit Investig. 2013;10:226–45.

    Article  Google Scholar 

  4. Al-Qershi OM, Khoo BE. Passive detection of copy–move forgery in digital images: state-of-the-art. Forensic Sci Int. 2013;231(1–3):284–95.

    Article  Google Scholar 

  5. Kakar P, Sudha N. Exposing postprocessed copy–paste forgeries through transform-invariant features. IEEE Trans Inform Forensics Secur. 2012;7:1018–28.

    Article  Google Scholar 

  6. Gana Y, Zhongc Jl, Vong C. A novel copy–move forgery detection algorithm via feature label matching and hierarchical segmentation filtering. Inform Process Manag. 2022. https://doi.org/10.1016/j.ipm.2021.102783.

    Article  Google Scholar 

  7. Pan X, Lyu S. Region duplication detection using image feature matching. IEEE Trans Inf Forensic Secur. 2010;5(4):857–67.

    Article  Google Scholar 

  8. Silva E, Carvalho T, Ferreira A, Rocha A. Going deeper into copy–move forgery detection: exploring image telltales via multi-scale analysis and voting processes. J Vis Commun Image Represent. 2015;29:16–32.

    Article  Google Scholar 

  9. Li J, Li X, Yang B, Sun X. Segmentation-based image copy–move forgery detection scheme. IEEE Trans Inf Forensic Secur. 2015;10(3):507–18.

    Article  Google Scholar 

  10. Warif NBA, Wahab AWA, Idris MYI, Salleh R, Othman F. Sift-symmetry: a robust detection method for copy–move forgery with reflection attack. J Vis Commun Image Represent. 2017;46:219–32.

    Article  Google Scholar 

  11. 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 Forensic Secur. 2011;6(3):1099–110.

    Article  Google Scholar 

  12. Jin G, Wan X. An improved method for sift-based copy– move forgery detection using non-maximum value suppression and optimized j-linkage. Signal Process-Image Commun. 2017;57:113–25.

    Article  Google Scholar 

  13. Amerini I, Ballan L, Caldelli R, Del Bimbo A, Del Tongo L, Serra G. Copy–move forgery detection and localization by means of robust clustering with j-linkage. Signal Process-Image Commun. 2013;28(6):659–69.

    Article  Google Scholar 

  14. Niu P, Wang C, Chen W, Yang H, Wang X. Fast and effective key point-based image copy–move forgery detection using complex-valued moment invariants. J Vis Commun Image Represent. 2021;77: 103068.

    Article  Google Scholar 

  15. Krawetz N, Hacker Factor Solutions. A picture’s worth. Hacker Factor Solutions; 2007.

    Google Scholar 

  16. Ferrara P, et al. Image forgery localization via fine-grained analysis of CFA artifacts. IEEE Trans Inform Forensics Secur. 2012;7(5):1566–77.

    Article  Google Scholar 

  17. Barni M, et al. Aligned and non-aligned double JPEG detection were using convolution neural networks. J Vis Commun Image Represent. 2017;49:153–63.

    Article  Google Scholar 

  18. Krawetz N, Hacker Factor Solutions. A picture’s worth. Hacker Factor Sol. 2007;6(2):2.

    Google Scholar 

  19. Fridrich J, Kodovsky J. Rich models for steganalysis of digital images. IEEE Trans Inform Forensics Secur. 2012;7(3):868–82.

    Article  Google Scholar 

  20. Lin Z, et al. Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis. Pattern Recognit. 2009;42(11):2492–501.

    Article  MATH  Google Scholar 

  21. Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E. An evaluation of popular copy–move forgery detection approaches. IEEE Trans Inform Forensics Secur. 2012;7(6):1841–54.

    Article  Google Scholar 

  22. Fridrich A, Soukal B, Lukas A. Detection of copy–move forgery in digital images. In: Proc. of Digital Forensic Research Workshop. Citeseer, 2003.

  23. Popescu AC, Farid H. Exposing digital forgeries by detecting duplicated image regions. Dept. Computer Science, Dartmouth College, Tech. Rep. TR2004–515, 2004.

  24. Nguyen HC, Katzenbeisser S. Security of copy–move forgery detection techniques. In: Proc. of the IEEE Int. Conference on Acoustics, Speech and Signal Processing (ICASSP11), 2011; pp. 1864–1867.

  25. Lyu S, Pan X, Zhang X. Exposing region splicing forgeries with blind local noise estimation. Int J Comput Vis. 2014;110(2):202–21.

    Article  Google Scholar 

  26. Pan X, Lyu S. Region duplication detection using image feature matching”. IEEE Trans Inform Forensics Secur. 2010;5(4):857–67.

    Article  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 Inform Forensics Secur. 2011;6(3):1099–110.

    Article  Google Scholar 

  28. Brighi R, Ferrazzano M. Digital forensics: best practices and perspective. Digital Forensics Evidence: Towards Common European Standards in Antifraud Administrative and Criminal Investigations, 2021; pp. 25–60.

  29. Carrier BD, Spafford EH. Defining event reconstruction of a digital crime science. J Forensic Sci. 2004;49(6):JFS2004127–8.

    Article  Google Scholar 

  30. Consultative Committee for Space Data Systems. Reference model for an open archival information system (OAIS). CCSDS Secretariat; 2002.

    Google Scholar 

  31. Gary B-C. Community discussion of the definition of digital object. Retrieved from Data Foundation and Terminology; 2014. https://www.rd-alliance.org/group/data-foundation-and-terminology-wg/post/community-discussion-definition-digital-object.html. Accessed 13 Aug 2014.

  32. Carrier BD, Spafford EH. An event-based digital forensic investigation framework. In: Digital Forensic Research Workshop (DFRWS USA), 2004.

  33. Casey E. Digital evidence and computer crime. 2nd ed. Elsevier; 2004.

    Google Scholar 

  34. Pica A. An overview on image forensics. International Scholarly Research Notices; 2013.

    Google Scholar 

  35. Burns M. A quick guide to digital image forensics in 2020. Retrieved from Camera Forensics Blog; 2020. https://www.cameraforensics.com/blog/2020/03/06/a-quick-guide-to-digital-image-forensics-in-2020/. Accessed 6 Mar 2020.

  36. Lord N. What is memory forensics? A definition of memory forensics, retrieved from data insider blog; 2020. https://digitalguardian.com/blog/what-are-memory-forensics-definition-memory-forensics. Accessed 29 Sept 2020.

  37. Chirath DA. Email forensics: investigation techniques. Retrieved from forensic. Focus Article; 2019. https://www.forensicfocus.com/articles/email-forensics-investigation-techniques. Accessed 15 Feb 2019.

  38. Banday MT. Techniques and tools for forensic investigation of E-mail. Int J Netw Secur Appl. 2011;3(6):227.

    Google Scholar 

  39. Lazic L, Bogdanoski M. E-mail forensics: techniques and tools for forensic investigation. In: 10th Intl. Conference on business information security, 2018; p. 25.

  40. Jing W, Hongbin Z. Exposing digital forgeries by detecting traces of image splicing. In: 8th International Conference on signal processing, vol. 2, IEEE, 2006.

  41. Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E. An evaluation of popular copy–move forgery detection approaches. IEEE Trans Inf Forensics Secur. 2012;7(6):1841–54.

    Article  Google Scholar 

  42. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, 2015; pp. 1–9.

  43. Fridrich AJ, Soukal BD, Luk´aˇs AJ. “Detection of copy–move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop. Cite seer, 2003.

  44. Ke Y, Sukthankar R, Huston L. An efficient parts-based near-duplicate and sub-image retrieval system. In: Proceeding of the 12th annual ACM International Conference on Multimedia, 2004; pp. 869–876.

  45. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. Advances in neural information processing systems, vol. 27; 2014.

  46. Zhu JY, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: IEEE ICCV, 2017.

  47. P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to image translation with conditional adversarial networks,” in IEEE CVPR, 2017.

  48. Cozzolino D, Poggi G, Verdoliva L. Efficient dense-field copy–move forgery detection. IEEE Trans Inf Forensic Secur. 2015;10(11):2284–97.

    Article  Google Scholar 

  49. Qi S, Zhang Y, Wang C, Zhou J, Cao X. A principled design of image representation: towards forensic tasks. 2022. 2203.00913v1.

  50. Kashyap A, Parmar RS, Agrawal M, Gupta H. An evaluation of digital image forgery detection approaches. 2017. arXiv preprint arXiv: 1703.09968.

  51. N. K. Gill, R. Garg, and E. A. Doegar, "A review paper on digital image forgery detection techniques," in Computing, Communication and Networking Technologies (ICCCNT), 8th International Conference, pp. 1–7, 2017.

  52. T. M. Mohammed, J. Bunk, L. Nataraj, J. H. Bappy, A. Flenner, B. Manjunath, et al., "Boosting Image Forgery Detection using Resampling Detection and Copy–move analysis," arXiv preprint arXiv: 1802.03154, 2018.

  53. Mayer O, Stamm MC. Accurate and efficient image forgery detection using lateral chromatic aberration. IEEE Trans Inf Forensics Secur. 2018;13:1762–77.

    Article  Google Scholar 

  54. Lin X, Li J-H, Wang S-L, Cheng F, Huang X-S. Recent advances in passive digital image security forensics: a brief review. Engineering. 2018;4:29–39.

    Article  Google Scholar 

  55. Jaiswal AK, Srivastava R. Image splicing detection using deep residual network. In: 2nd International Conference on advanced computing and software engineering, ICACSE, 2019.

  56. Xiao B, Wei Y, Bi X, Li W, Ma J. Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering. Elsevier Inform Sci. 2019;511:172–91.

    Article  MathSciNet  Google Scholar 

  57. Le-Tien T, Phan-Xuan H, Nguyen-Chinh T, Do-Tieu T. Image forgery detection: a low computational-cost and effective data-driven model. Int J Mach Learn Comput. 2019;9(2):181–8.

    Article  Google Scholar 

  58. Boato G, Dang-Nguyen D-T, Denatale FGB. Morphological filter detector for image forensics applications. iEEE Access. 2020;8:13549–60.

    Article  Google Scholar 

  59. Liu Y, Zhao X. Constrained image splicing detection and localization with attention-aware encoder-decoder and atrous convolution. IEEE Access. 2020;8:6729–41.

    Article  Google Scholar 

  60. Rao Y, Ni J, Zhao H. Deep learning local descriptor for image splicing detection and localization. IEEE Access. 2020;8:25611–25.

    Article  Google Scholar 

  61. Lin C, et al. Copy–move forgery detection using combined features and transitive matching. Multimed Tools Appl. 2018;78(21):30081–96.

    Article  Google Scholar 

  62. Wang C, Zhang Z, Zhou X. An image copy–move forgery detection scheme based on akaze and surf features. Symmetry. 2018;10(12):706.

    Article  MATH  Google Scholar 

  63. Zhang W, Yang Z, Niu S, Wang J. Detection of copy–move forgery in flat region based on feature enhancement. In: Shi Y, Kim H, Perez-Gonzalez F, Liu F, editors. Digital forensics and watermarking, IWDW. Lecture Notes in Computer Science, vol. 10082. Cham: Springer; 2017. p. 159–71.

    Chapter  Google Scholar 

  64. Abdul Warif NB, Abdul Wahab AW, Idna Idris MY, Fazidah Othman RS. SIFT-Symmetry: A robust detection method for copy–move forgery with a reflection attack. J Vis Commun Image Represent. 2017;46:219–32.

    Article  Google Scholar 

  65. Venkateswara A, Srinivasa C, Cheruku DR. An innovative and efficient deep learning algorithm for copy move forgery detection in digital images. Int J Adv Sci Technol. 2020;29(05):10531–42.

    Google Scholar 

  66. Meena KB, Tyagi V. A hybrid copy–move image forgery detection technique based on Fourier–Mellin and scale-invariant feature Transforms. Multimed Tools Appl. 2020. https://doi.org/10.1007/s11042-019-08343-0.

    Article  Google Scholar 

  67. Wu Y, Abd-Almageed W, Natarajan P. BusterNet: detecting copy–move image forgery with source/target localization. In: European Conference on Computer Vision, ECCV 2018: Computer Vision–ECCV, 2018; pp 170–186.

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Correspondence to Manuj Mishra.

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Mishra, M., Jain, N.K. & Kumar, A. Reviewing Image Data: Detecting Forgery Using a Robust Forensic Method. SN COMPUT. SCI. 4, 856 (2023). https://doi.org/10.1007/s42979-023-02287-x

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