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Detection of copy-move image forgery based on discrete cosine transform

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Abstract

Since powerful editing software is easily accessible, manipulation on images is expedient and easy without leaving any noticeable evidences. Hence, it turns out to be a challenging chore to authenticate the genuineness of images as it is impossible for human’s naked eye to distinguish between the tampered image and actual image. Among the most common methods extensively used to copy and paste regions within the same image in tampering image is the copy-move method. Discrete Cosine Transform (DCT) has the ability to detect tampered regions accurately. Nevertheless, in terms of precision (FP) and recall (FN), the block size of overlapping block influenced the performance. In this paper, the researchers implemented the copy-move image forgery detection using DCT coefficient. Firstly, by using the standard image conversion technique, RGB image is transformed into grayscale image. Consequently, grayscale image is segregated into overlying blocks of m × m pixels, m = 4.8. 2D DCT coefficients are calculated and reposition into a feature vector using zig-zag scanning in every block. Eventually, lexicographic sort is used to sort the feature vectors. Finally, the duplicated block is located by the Euclidean Distance. In order to gauge the performance of the copy-move detection techniques with various block sizes with respect to accuracy and storage, threshold D_similar = 0.1 and distance threshold (N)_d = 100 are used to implement the 10 input images in order. Consequently, 4 × 4 overlying block size had high false positive thus decreased the accuracy of forged detection in terms of accuracy. However, 8 × 8 overlying block accomplished more accurately for forged detection in terms of precision and recall as compared to 4 × 4 overlying block. In a nutshell, the result of the accuracy performance of different overlying block size are influenced by the diverse size of forged area, distance between two forged areas and threshold value used for the research.

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References

  1. Pinsky LE, Wipf JE (2000) A picture is worth a thousand words. J Gen Int Med 15:805–810

    Article  Google Scholar 

  2. Norouzi A, Rahim MSM, Altameem A, Saba T, Rada AE, Rehman A, Uddin M (2014) Medical image segmentation methods, algorithms, and applications. IETE Tech Rev 31(3):199–213. doi:10.1080/02564602.2014.906861

    Article  Google Scholar 

  3. Mundher M, Muhamad D, Rehman A, Saba T, Kausar F (2014) Digital watermarking for images security using discrete slant let transform. Appl Math Inf Sci 8(6):2823–2830. doi:10.12785/amis/080618

    Article  Google Scholar 

  4. Belk RW (2013) Extended self in a digital world. J Consum Res 40:477–500

    Article  Google Scholar 

  5. Saba T, Rehman A, Sulong G (2011) Cursive script segmentation with neural confidence. Int J Innov Comput Inf Control (IJICIC) 7(7):1–10

    Google Scholar 

  6. Rehman A, Saba T (2014) Features extraction for soccer video semantic analysis: current achievements and remaining issues. Artif Intell Rev 41(3):451–461. doi:10.1007/s10462-012-9319-1

    Article  Google Scholar 

  7. Karie NM, Venter HS (2014) Toward a general ontology for digital forensic disciplines. J Forensic Sci 59:1231–1241

    Article  Google Scholar 

  8. Al-Qershi OM, Khoo BE (2013) Passive detection of copy-move forgery in digital images: state-of-the-art. Forensic Sci Int 231:284–295

    Article  Google Scholar 

  9. Rehman A, Saba T (2012) Off-line cursive script recognition: current advances, comparisons and remaining problems. Artif Intell Rev 37(4):261–288. doi:10.1007/s10462-011-9229-7

    Article  Google Scholar 

  10. Anand V, Hashmi MF, Keskar AG (2014) A copy move forgery detection to overcome sustained attacks using dyadic wavelet transform and sift methods. In: Intelligent information and database systems. Lecture Notes in Computer Science, vol 8397. Springer, pp 530–542

  11. Muhsin ZF, Rehman A, Altameem A, Saba T, Uddin M (2014) Improved quadtree image segmentation approach to region information. Imaging Sci J 62(1):56–62. doi:10.1179/1743131X13Y.0000000063

    Article  Google Scholar 

  12. Zhao Y, Sutardja A, Ramadan O (2015) Digital image manipulation forensic. Technical Report No. UCB/EECS-2015-125, Electrical Engineering and Computer Sciences, University of California at Berkeley

  13. Sutardja A, Ramadan O, Zhao Y (2015) Forensic methods for detecting image manipulation-copy move. Technical Report No. UCB/EECS-2015-84, Electrical Engineering and Computer Sciences, University of California at Berkeley

  14. Yazdani S et al (2015) Image segmentation methods and applications in MRI brain images. IETE Tech Rev 32:413–427

    Article  Google Scholar 

  15. Saba T, Rehman A (2012) Machine learning and script recognition. Lambert Academic publisher, pp 39–45. ISBN-13: 978-3659111709

  16. Saba T, Rehman A, Altameem A, Uddin M (2014) Annotated comparisons of proposed preprocessing techniques for script recognition. Neural Comput Appl 25(6):1337–1347. doi:10.1007/s00521-014-1618-9

    Article  Google Scholar 

  17. Granty REJ, Aditya T, Madhu SS (2010) Survey on passive methods of image tampering detection. In: Communication and computational intelligence (INCOCCI), 2010 international conference on, pp 431–436

  18. Mire AV et al (2014) Digital forensic of JPEG images. In: Signal and image processing (ICSIP), 2014 fifth international conference on 2014, pp 131–136

  19. Rehman A, Saba T (2014) Evaluation of artificial intelligent techniques to secure information in enterprises. Artif Intell Rev 42(4):1029–1044. doi:10.1007/s10462-012-9372-9

  20. Saba T, Rehman A, Al-Dhelaand A, Al-Rodhaand M (2014) Evaluation of current documents image denoising techniques: a comparative study. Appl Artif Intell 28(9):879–887. doi:10.1080/08839514.2014.954344

  21. Joudaki S, Mohamad D, Saba T, Rehman A, Al-Rodhaan M, Al-Dhelaan A (2014) Vision-based sign language classification: a directional review. IETE Tech Rev 31(5):383–391. doi:10.1080/02564602.2014.961576

    Article  Google Scholar 

  22. Fadhil MS, Alkawaz MH, Rehman A, Saba T (2016) Writers identification based on multiple windows features mining. 3D Res 7(1):1–6. doi:10.1007/s13319-016-0087-6

  23. Bayram S, Sencar HT, Memon N (2009) An efficient and robust method for detecting copy-move forgery. In: Acoustics, speech and signal processing, 2009. ICASSP 2009. IEEE international conference on 2009, pp 1053–1056

  24. Meethongjan K, Dzulkifli M, Rehman A, Altameem A, Saba T (2013) An intelligent fused approach for face recognition. J Intell Syst 22(2):197–212. doi:10.1515/jisys-2013-0010

    Google Scholar 

  25. Al-Ameen Z, Sulong G, Rehman A, Al-Dhelaan A, Saba T, Al-Rodhaan M (2015) An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization. EURASIP J Adv Signal Process 32:1–12. doi:10.1186/s13634-015-0214-1

    Google Scholar 

  26. Basori AH, Alkawaz MH, Saba T, Rehman A (2016) An overview of interactive wet cloth simulation in virtual reality and serious games. Comput Methods Biomech Biomed Eng Imaging Vis. doi:10.1080/21681163.2016.1178600

    Google Scholar 

  27. Mahdian B, Saic S (2010) A bibliography on blind methods for identifying image forgery. Signal Process Image Commun 25:389–399

    Article  Google Scholar 

  28. Saba T, Rehman A, Al-Dhelaan A, Al-Rodhaan M (2014) Evaluation of current documents image denoising techniques: a comparative study. Appl Artif Intell 28(9):879–887. doi:10.1080/08839514.2014.954344

    Article  Google Scholar 

  29. Pan X, Lyu S (2010) Region duplication detection using image feature matching. Inf Forensics Secur IEEE Trans 5:857–867

    Article  Google Scholar 

  30. Ahmad AM, Sulong G, Rehman A, Alkawaz MH, Saba T (2014) Data hiding based on improved exploiting modification direction method and Huffman coding. J Intell Syst 23(4):451–459. doi:10.1515/jisys-2014-0007

    Google Scholar 

  31. Amerini I et al (2011) A sift-based forensic method for copy-move attack detection and transformation recovery. Inf Forensics Secur IEEE Trans 6:1099–1110

    Article  Google Scholar 

  32. Boato G, Natale F, Zontone P (2010) How digital forensics may help assessing the perceptual impact of image formation and manipulation. In: Proceedings of fifth international workshop on video processing and quality metrics for consumer electronics–VPQM, 2010

  33. Nodehi A, Sulong G, Al-Rodhaan M, Al-Dhelaan A, Rehman A, Saba T (2014) Intelligent fuzzy approach for fast fractal image compression. EURASIP J Adv Signal Process. doi:10.1186/1687-6180-2014-112

  34. Petitcolas FA, Anderson RJ, Kuhn MG (1999) Information hiding—a survey. Proc IEEE 87:1062–1078

    Article  Google Scholar 

  35. Christlein V, Riess C, Angelopoulou E (2010) A study on features for the detection of copy-move forgeries. Sicherheit 2010:105–116

    Google Scholar 

  36. Lu W, Wu M (2010) Multimedia forensic hash based on visual words. In: Image processing (ICIP), 2010 17th IEEE international conference on 2010, pp 989–992

  37. Verma VS, Jha RK (2015) An overview of robust digital image watermarking. IETE Tech Rev 32:479–496

    Article  Google Scholar 

  38. Christlein V et al (2012) An evaluation of popular copy-move forgery detection approaches. Inf Forensics Secur IEEE Trans 7:1841–1854

    Article  Google Scholar 

  39. Sunil K, Jagan D, Shaktidev M (2014) DCT-PCA based method for copy-move forgery detection. In: ICT and critical infrastructure: proceedings of the 48th annual convention of computer society of India, vol II, pp 577–583

  40. Farid H, Lyu S (2003) Higher-order wavelet statistics and their application to digital forensics. In: IEEE Workshop on Statistical Analysis in Computer Vision (in conjunction with CVPR), 2003

  41. Khan S, Kulkarni A (2010) Robust method for detection of copy-move forgery in digital images. In: Signal and image processing (ICSIP), 2010 international conference on 2010, pp 69–73

  42. Sridevi M, Mala C, Sanyam S (2012) Comparative study of image forgery and copy-move techniques. In: Advances in computer science, engineering and applications. Advances in Intelligent and Soft Computing, vol 166. Springer, pp 715–723

  43. Fridrich AJ, Soukal BD, Lukáš AJ (2003) Detection of copy-move forgery in digital images. In: Proceedings of digital forensic research workshop, 2003

  44. Mahmood T, Nawaz T, Irtaza A, Ashraf R, Shah M, Mahmood MT (2016) Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images. Math Probl Eng 2016(2016) ID 8713202, doi:10.1155/2016/8713202

  45. Pun CM, Yuan X-C, Bi X-L (2015) Image forgery detection using adaptive over segmentation and feature point matching. Inf Forensics Secur IEEE Trans 10:1705–1716

    Article  Google Scholar 

  46. Yan CP, Pun C-M, Yuan X-C (2016) Multi-scale image hashing using adaptive local feature extraction for robust tampering detection. Signal Process 121:1–16

    Article  Google Scholar 

  47. Cao Y, Gao T, Fan L, Yang Q (2012) A robust detection algorithm for copy-move forgery in digital images. Forensic Sci Int 214(1-3):33–34

  48. Yavuz F, Bal A, Cukur H (2016) An effective detection algorithm for region duplication forgery in digital images. Proc. SPIE 9845, Optical Pattern Recognition XXVII, 98450O. doi:10.1117/12.2223732

  49. Zampoglou M, Papadopoulos S, Kompatsiaris Y (2015) Detecting image splicing in the wild (WEB). In: Multimedia and expo workshops (ICMEW), 2015 IEEE international conference on 2015, pp 1–6

Download references

Acknowledgments

Authors are grateful to Faculty of Information Sciences and Engineering, Management and Science University (MSU), Shah Alam, Selangor and Faculty of Computing, Universiti Teknologi Malaysia (UTM), Skudai 81310 Johor, Malaysia for their support in this research.

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Correspondence to Amjad Rehman.

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Alkawaz, M.H., Sulong, G., Saba, T. et al. Detection of copy-move image forgery based on discrete cosine transform. Neural Comput & Applic 30, 183–192 (2018). https://doi.org/10.1007/s00521-016-2663-3

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