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Robust Detection Algorithm for Copy-Move Forgery in Digital Images

This paper introduces a robust detection algorithm for identifying copy-move forgery in digital images using DCT coefficients. The proposed scheme includes block dividing, feature extraction, and matching to detect tampered images efficiently. Experimental results demonstrate the effectiveness of the algorithm in detecting multiple copy-move forgery instances with added noise or blurring. The algorithm provides automated and reliable forgery detection, ensuring accuracy in identifying duplicated regions. References to related works are also included for further context.

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Robust Detection Algorithm for Copy-Move Forgery in Digital Images

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  1. A robust detection algorithm for copy-move forgery in digital images 1 Source: Forensic Science International, Volume 214, Issues 1–3, 10 January 2012 Authors: Yanjun Cao, Tiegang Gao, Li Fan, Qunting Yang Presenter: Li-Ting Liao Date: 2012/06/14

  2. OUTLINE 2 • Introduction • Proposed Scheme • Experimental Results • Conclusions

  3. Copy-move forgery Original image Tamper image detect Introduction 3

  4. Flowchart of the proposed scheme Proposed Scheme 4

  5. B B B B N N THE PROPOSED SCHEME – Block Dividing (1/2) 5 Generate (N-B+1)(N-B+1) Blocks

  6. THE PROPOSED SCHEME –Block Dividing (2/2) 6 Block size : 4 × 4 … Original image

  7. DCT Transform THE PROPOSED SCHEME – DCT transform 7 DCT coefficient block Original block

  8. Generate matching feature ： C1 C2 C4 C3 THE PROPOSED SCHEME – Feature extraction (1/2) 8 DCT coefficient block

  9. C1 C2 ≒ 145.2746 C4 C3 ≒ 0.8715 ≒ -0.0095 ≒ -0.7716 THE PROPOSED SCHEME – Feature extraction (2/2) 9 Generate matching feature ： DCT coefficient block

  10. Similar condition ： (x1, y1) 1 2X2 (x2, y2) d 2 2X2 THE PROPOSED SCHEME – Matching (1/3) 10

  11. Not Similar THE PROPOSED SCHEME – Matching (2/3) 11

  12. ≒ 127.28 Similar Detected image THE PROPOSED SCHEME – Matching (3/3) 12

  13. EXPERIMENTAL RESULTS(1/6) 13 The detection results (from left to right is the original image, tampered image, detection results).

  14. EXPERIMENTAL RESULTS(2/6) 14 The detection results for non-regular copy-move forgery

  15. EXPERIMENTAL RESULTS(3/6) 15 The test results for multiple copy-move forgery under a mixed operation

  16. EXPERIMENTAL RESULTS(4/6) 16 The top row are tampered images with duplicated region size of 32 pixels × 32 pixels. Shown below are the detection results using our algorithm

  17. (a) (b) EXPERIMENTAL RESULTS(5/6) 17 DAR curves for DCT, DCT-improved, PCA, FMT, and Proposed methods when the duplicated region is 64 pixels 64 pixels. (a) Gaussian noise, and (b) Gaussian blurring

  18. EXPERIMENTAL RESULTS(6/6) 18 [2] A. Fridrich, et al., Detection of Copy-move Forgery in Digital Images, 2003. [3] Y. Huang, et al., Improved DCT-based detection of copy-move forgery in images, Forensic Science International 206 (1–3) (2011) 178–184. [4] A. Popescu and H. Farid, Exposing digital forgeries by detecting duplicated image regions, Dept. Comput. Sci., Dartmouth College, Tech. Rep. TR2004-515, 2004.

  19. CONCLUSIONS 19 • This paper presented an automatic and efficient detection algorithm for copy-move forgery • The proposed algorithm could not only endure the multiple copy-move forgery, but also the blurring or nosing adding

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