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An integrated method of copy-move and splicing for image forgery detection

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Abstract

Splicing and copy-move are two well known methods of passive image forgery. In this paper, splicing and copy-move forgery detection are performed simultaneously on the same database CASIA v1.0 and CASIA v2.0. Initially, a suspicious image is taken and features are extracted through BDCT and enhanced threshold method. The proposed technique decides whether the given image is manipulated or not. If it is manipulated then support vector machine (SVM) classify that the given image is gone through splicing forgery or copy-move forgery. For copy-move detection, ZM-polar (Zernike Moment) is used to locate the duplicated regions in image. Experimental results depict the performance of the proposed method.

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References

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

    Article  Google Scholar 

  2. Amerini I, Ballan L, Caldelli R, Bimbo AD, Tongo LD, Serra G (2013) Copy-move forgery detection and localization by means of robust clustering with j-linkage. Signal Process Image Commun 28(6):659–669

    Article  Google Scholar 

  3. Andreopoulos A, Tsotsos JK (2013) 50 years of object recognition directions forward. Comput Vis Image Underst 117(8):827–891

    Article  Google Scholar 

  4. Barnes C, Shechtman E, Finkelstein A, Goldman DB (2009) Patchmatch: A randomized correspondence algorithm for structural image editing. ACM Trans Graph 28(3):24–1

    Article  Google Scholar 

  5. 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, pp 1053–1056. IEEE

  6. Bianchi T, Piva A (2012) Image forgery localization via block-grained analysis of jpeg artifacts. IEEE Trans Inf Forensics Secur 7(3):1003–1017

    Article  Google Scholar 

  7. Bo S, Yuan Q, Wang S, Zhao C, Li S (2014) Enhanced state selection markov model for image splicing detection. EURASIP J Wirel Commun Netw 2014(1):7

    Article  Google Scholar 

  8. Bravo-Solorio S, Nandi AK (2011) Automated detection and localisation of duplicated regions affected by reflection, rotation and scaling in image forensics. Signal Process 91(8):1759–1770

    Article  MATH  Google Scholar 

  9. Campos FM, Correia L, Calado JMF (2015) Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches. J Intell Rob Syst 77(2):377–390

    Article  Google Scholar 

  10. Cao Y, Gao T, Li F, Yang Q (2012) A robust detection algorithm for copy-move forgery in digital images. Forensic Sci Int 214(1):33–43

    Article  Google Scholar 

  11. Chen L, Wei L, Ni J, Sun W, Huang J (2013) Region duplication detection based on harris corner points and step sector statistics. J Vis Commun Image Represent 24(3):244–254

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Dollar P, Wojek C, Schiele B, Perona P (2012) Pedestrian detection: An evaluation of the state of the art. IEEE Trans Pattern Anal Mach Intell 34(4):743–761

    Article  Google Scholar 

  14. Dong J, Wang W, Tan T (2013) Casia image tampering detection evaluation database. In: Signal and information processing (ChinaSIP), 2013 IEEE China Summit & International Conference on, pp 422–426. IEEE

  15. El-Alfy E-SM, Qureshi MA (2015) Combining spatial and dct based markov features for enhanced blind detection of image splicing. Pattern Anal Applic 18 (3):713–723

    Article  MathSciNet  Google Scholar 

  16. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  17. Fridrich AJ, Soukal BD, Lukáš AJ (2003) Detection of copy-move forgery in digital images. In Inproceedings of Digital Forensic Research Workshop. Citeseer

  18. Gionis A, Indyk P, Motwani R et al. (1999) Similarity search in high dimensions via hashing. In: VLDB, vol. 99, pp 518–529

  19. Guo J-M, Liu Y-F, Wu Z-J (2013) Duplication forgery detection using improved daisy descriptor. Expert Syst Appl 40(2):707–714

    Article  Google Scholar 

  20. Hakimi F (2015) Image-splicing forgery detection based on improved lbp and k-nearest neighbors algorithm. Electron Inf Plan, 3

  21. He Z, Wei L, Sun W, Huang J (2012) Digital image splicing detection based on markov features in dct and dwt domain. Pattern Recogn 45(12):4292–4299

    Article  Google Scholar 

  22. Hu W-C, Dai J-S, Jian J-S (2015) Effective composite image detection method based on feature inconsistency of image components. Digital Signal Process 39:50–62

    Article  Google Scholar 

  23. Huang Y, Wei L, Sun W, Long D (2011) Improved dct-based detection of copy-move forgery in images. Forensic Sci Int 206(1):178–184

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Jain AK, Ross AA, Nandakumar K (2011) Introduction. In: Introduction to Biometrics, pp 1–49. Springer

  26. Kakar P, Sudha N (2012) Exposing postprocessed copy–paste forgeries through transform-invariant features. IEEE Trans Inf Forensics Secur 7(3):1018–1028

    Article  Google Scholar 

  27. Kodovsky J, Fridrich J, Holub V (2012) Ensemble classifiers for steganalysis of digital media. IEEE Trans Inf Forensics Secur 7(2):432–444

    Article  Google Scholar 

  28. Langille A, Gong M (2006) An efficient match-based duplication detection algorithm. In: Computer Robot Vision The 3rd Canadian Conference on, pp 64–64. IEEE

  29. Lazebnik S, Schmid C, Ponce J (2005) A sparse texture representation using local affine regions. IEEE Trans Pattern Anal Mach Intell 27(8):1265–1278

    Article  Google Scholar 

  30. Li Y (2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Sci Int 224(1):59–67

    Article  Google Scholar 

  31. Li X, Jing T, Li X (2010) Image splicing detection based on moment features and hilbert-huang transform. In: 2010 IEEE international conference on information theory and information security (ICITIS), pp 1127–1130. IEEE

  32. Li L, Li S, Zhu H, Wu X (2014) Detecting copy-move forgery under affine transforms for image forensics. Comput Electr Eng 40(6):1951–1962

    Article  Google Scholar 

  33. Li L, Li C, Ye L, Jia Y, Rosenblum DS (2016) Recognizing complex activities by a probabilistic interval-based model. In: AAAI, vol. 30, pp 1266–1272

  34. Liu S, Bai X (2012) Discriminative features for image classification and retrieval. Pattern Recogn Lett 33(6):744–751

    Article  Google Scholar 

  35. Lu Y, Ye W, Li L, Zhong J, Sun L, Ye L (2017) Towards unsupervised physical activity recognition using smartphone accelerometers. Multimedia Tools and Applications 76(8):10701–10719

    Article  Google Scholar 

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

    Article  Google Scholar 

  37. Moreels P, Perona P (2007) Evaluation of features detectors and descriptors based on 3d objects. Int J Comput Vis 73(3):263–284

    Article  Google Scholar 

  38. Muhammad G, Hussain M, Bebis G (2012) Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digit Investig 9(1):49–57

    Article  Google Scholar 

  39. Muja M, Lowe DG (2009) Fast approximate nearest neighbors with automatic algorithm configuration. VISAPP (1) 2(331-340):2

    Google Scholar 

  40. Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensics Secur 5(4):857–867

    Article  Google Scholar 

  41. Pevny T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensics Secur 5(2):215–224

    Article  Google Scholar 

  42. Pun C-M, Bo L, Yuan X-C (2016) Multi-scale noise estimation for image splicing forgery detection. J Vis Commun Image Represent 38:195–206

    Article  Google Scholar 

  43. Qiu X, Li H, Luo W, Huang J (2014) A universal image forensic strategy based on steganalytic model. In: Proceedings of the 2nd ACM workshop on Information hiding and multimedia security, pages 165–170. ACM

  44. Rahmani R, Goldman SA, Zhang H, Krettek J, Fritts JE (2005) Localized content based image retrieval. In: Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval, pp 227–236. ACM

  45. Ryu S-J, Lee M-J, Lee H-K (2010) Detection of copy-rotate-move forgery using zernike moments. In: Information hiding, vol. 6387, pp 51–65. Springer

  46. Ryu S-J, Kirchner M, Lee M-J, Lee H-K (2013) Rotation invariant localization of duplicated image regions based on zernike moments. IEEE Trans Inf Forensics Secur 8(8):1355–1370

    Article  Google Scholar 

  47. Shi Y, Chen C, Chen W (2007) A natural image model approach to splicing detection. In: Proceedings of the 9th workshop on Multimedia & security, pp 51–62. ACM

  48. Shivakumar BL, Baboo S (2011) Detection of region duplication forgery in digital images using surf. Int J Comput Sci Issues 8(4):199–205

    Google Scholar 

  49. Sutthiwan P, Shi Y, Su W, Ng T-T (2010) Rake transform and Edge statistics for image forgery detection. In: Multimedia and Expo (ICME), 2010 17th IEEE International Conference on, pp 1463–1468. IEEE

  50. Sutthiwan P, Shi Y-Q, Dong J, Tan T, Ng T-T (2010) New developments in color image tampering detection. In: Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, pp 3064–3067. IEEE

  51. Sutthiwan P, Shi Y, Zhao H, Ng T-T, Su W (2011) Markovian rake transform for digital image tampering detection. Transactions on data hiding and multimedia security VI 6:1–17

    Google Scholar 

  52. Wang W, Dong J, Tan T (2009) Effective image splicing detection based on image chroma. Image Processing (ICIP), 2009 16th IEEE International Conference on, pp 1257–1260. IEEE

  53. Wang W, Dong J, Tan T (2010) Image tampering detection based on stationary distribution of markov chain. In: Image Processing (ICIP), 2010 17th IEEE International Conference on, pp 2101–2104. IEEE

  54. Wu X, Fang Z (2011) Image splicing detection using illuminant color inconsistency. In: 2011 3rd international conference on multimedia information networking and security (MINES), pp 600–603. IEEE

  55. Xin Y, Pawlak M, Liao S (2007) Accurate computation of zernike moments in polar coordinates. IEEE Trans Image Process 16(2):581–587

    Article  MathSciNet  Google Scholar 

  56. Yap P-T, Jiang X, Kot AC (2010) Two-dimensional polar harmonic transforms for invariant image representation. IEEE Trans Pattern Anal Mach Intell 32(7):1259–1270

    Article  Google Scholar 

  57. Ye L, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2activity: Recognizing complex activities from sensor data, In: IJCAI, pp 1617–1623

  58. Ye L, Nie L, Li L, Rosenblum DS (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  59. Ye L, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune teller: Predicting your career path, In: AAAI, pp 201–207

  60. Zhang Q, Wei L, Weng J (2016) Joint image splicing detection in dct and contourlet transform domain. J Vis Commun Image Represent 40:449–458

    Article  Google Scholar 

  61. Zhao J, Zhao W (2013) Passive forensics for region duplication image forgery based on harris feature points and local binary patterns. Math Probl Eng 2013:12

    Google Scholar 

  62. Zhao X, Li J, Li S, Wang S (2011) Detecting digital image splicing in chroma spaces. Digital Watermarking 6526:12–22

    Article  Google Scholar 

  63. Zhao X, Wang S, Li S, Li J (2015) Passive image-splicing detection by a 2-d noncausal markov model. IEEE Trans Circuits Syst Video Technol 25(2):185–199

    Article  Google Scholar 

  64. Zou D, Shi YQ, Su W, Xuan G (2006) Steganalysis based on markov model of thresholded prediction-error image. In: Multimedia and Expo, 2006 IEEE International Conference on, pp 1365–1368. IEEE

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India

    Choudhary Shyam Prakash, Avinash Kumar, Sushila Maheshkar & Vikas Maheshkar

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  1. Choudhary Shyam Prakash
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  2. Avinash Kumar
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  4. Vikas Maheshkar
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Correspondence to Choudhary Shyam Prakash.

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Prakash, C.S., Kumar, A., Maheshkar, S. et al. An integrated method of copy-move and splicing for image forgery detection. Multimed Tools Appl 77, 26939–26963 (2018). https://doi.org/10.1007/s11042-018-5899-3

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