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A comprehensive survey of detecting tampered images and localization of the tampered region

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Abstract

Digital image fraudulent has become a very dangerous problem because of the rapidly growing media editing software. The information on the digital image can be easily replaced by fake information. Several cybercrime activities are attempted by hacking the digital images. Due to the presence of various kinds of media editing software, the digital image authenticity is very much significant. Nowadays, the detection of the manipulated images and localization of the manipulated regions are important issues. Many efforts have been made over the past decade to detect the tampered images and localization of the tampered regions with high accuracy based on some specially designed mechanisms. This paper presents a detailed survey of existing approaches for detecting tampered images and localization of the tampered regions.

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References

  1. Ahmed, Belal, and T Aaron Gulliver (2020). “Image splicing detection using mask-RCNN.” Signal, Image and Video Processing: 1–8

  2. Alahmadi A et al (2017) Passive detection of image forgery using DCT and local binary pattern. SIViP 11(1):81–88

    Article  Google Scholar 

  3. Alkawaz MH, Sulong G, Saba T, Rehman A (2018) Detection of copy-move image forgery based on discrete cosine transform. Neural Comput & Applic 30(1):183–192

    Article  Google Scholar 

  4. Ardizzone, Edoardo, Alessandro Bruno, and Giuseppe Mazzola (2010). “Detecting multiple copies in tampered images.” 2010 IEEE International Conference on Image Processing. IEEE

  5. Alahmadi AA, Hussain M, Aboalsamh H, Muhammad G, Bebis G (2013). Splicing image forgery detection based on DCT and local binary pattern, pp 253–256

  6. Beier, T, Neely, S (1992). Feature-based image metamorphosis. In: Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1992, pp. 35–42. ACM, New York

  7. Bilmes, J (1997). “A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models,” ICSI, Tech. Rep. TR-97-021

  8. Bay H, Tuytelaars T, Van GL (2006) SURF: speeded up robust features. Eur Conf Comput Vis 5:404–417

    Google Scholar 

  9. Bayar, Belhassen, and Matthew C. Stamm (2016). “A deep learning approach to universal image manipulation detection using a new convolutional layer.” Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security. ACM

  10. Barnes CE, Shechtman AF, Goldman D (2009) PatchMatch: A randomized correspondence algorithm for structural image editing. ACM Trans. Graph 28(3):24:1–24:11

    Article  Google Scholar 

  11. Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans. Intelligent Systems and Technology 2(3):27:1–27:27

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  14. Chen H, Yang X, Lyu Y (2020) Copy-move forgery detection based on Keypoint clustering and similar neighborhood search algorithm. IEEE Access 8:36863–36875

    Article  Google Scholar 

  15. Cozzolino. D, D Gragnaniello, and L Verdoliva (2014). “Image forgery localization through the fusion of camera-based, feature-based and pixelbased techniques,” in Proc. IEEE Int. Conf. Image Process., Oct. 2014,pp. 5302–5306

  16. Di H, Caifeng S (2011) Local binary patterns and its application to facial image analysis: a survey. IEEE Trans Syst Man Cybern 41(6):765–781

    Article  Google Scholar 

  17. Guo Y, Cao X, Zhang W, Wang R (2018) Fake colorized image detection. IEEE Transactions on Information Forensics and Security 13(8):1932–1944

    Article  Google Scholar 

  18. Girshick, R (2015). Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile, Dec 13–16

  19. Gaborini. L, P Bestagini, S Milani, M Tagliasacchi, and S Tubaro (2014).“Multi-clue image tampering localization,” in Proc. IEEE Int. Workshop Inf. Forensics Secur., Dec. 2014, pp. 125–130

  20. He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Analysis and Machine Intelligence 33(12):2341–2353

    Article  Google Scholar 

  21. Hussain, M, Wajid, SK, Elzaart, A, Berbar, M. (2011). A comparison of SVM kernel functions for breast cancer detection. In: Proceedings of the 2011 Eighth International Conference on Computer Graphics, Imaging and Visualization (CGIV 2011), Singapore, pp. 145–150

  22. He, K, et al. (2017). Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision. Honolulu, HI, USA, Jul 22–25

  23. He, K, et al.(2016). Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA, Jun 26–Jul 1

  24. Hashmi MF, Anand V, Keskar AG (2014). Copy-move image forgery detection using an efficient and robust method combining un-decimated wavelet transform and scale invariant feature transform, ELSEVIER, AASRI Conf. Circuit Signal Process., pp. 84–91

  25. Hu W-C, Chen W-H, Huang D-Y, Yang C-Y (2016) Effective image forgery detection of tampered foreground or background image based on image watermarking and alpha mattes. Multimed Tools Appl 75(6):3495–3516

    Article  Google Scholar 

  26. Hinton, GE, N Sriv astava, A Krizhevsky, I Sutskever, and RR Salakhutdinov (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580

  27. Kim C, Shin D, Yang C-N (2018) Self-embedding fragile watermarking scheme to restoration of a tampered image using AMBTC. Pers Ubiquit Comput 22(1):11–22

    Article  Google Scholar 

  28. Kohale T, Chede SD and Lakhe PR (2015). “Forgery of Copy Move Image Detection Technique by Integrating Block and Feature Based Method.” International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 1, January

  29. King DE (2009) Dlib-ml: a machine learning toolkit. J Mach Learn Res 10:1755–1758

    Google Scholar 

  30. Kazemi, V, Sullivan, J (2014). One millisecond face alignment with an ensemble of regression trees. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2014, Computer Society, pp. 1867–1874. IEEE, Washington, DC

  31. Kumar S, Nagori S (2017) Key-point based copy-move forgery detection in digital images. Journal of Statistics and Management Systems 20(4):611–621

    Article  Google Scholar 

  32. Krizhevsky, A I Sutskever, and GE Hinton (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097{1105

  33. Kumar, Manoj, Sangeet Srivastava, and Nafees Uddin. “Forgery detection using multiple light sources for synthetic images.” Australian Journal of Forensic Sciences (2017): 1–8

  34. Laouamer L, Tayan O (2018) Performance evaluation of a document image watermarking approach with enhanced tamper localization and recovery. IEEE Access 6:26144–26166

    Article  Google Scholar 

  35. Li J et al (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Transactions on Information Forensics and Security 10(3):507–518

    Article  Google Scholar 

  36. Lee BB, Pokorny J (1990) Luminance and chromatic modulation sensitivity of macaque ganglion cells and human observers. JOSA A 7:2223–2236

    Article  Google Scholar 

  37. Li Y, Zhou J (2019) Fast and effective image copy-move forgery detection via hierarchical feature point matching. IEEE Trans. Inf. Forensics Security 14(5):13071322

    Google Scholar 

  38. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. International journal of computer vision60 2:91–110

    Article  Google Scholar 

  39. Leutenegger S, Chli M (2011). BRISK: Binary Robust Invariant Scalable Keypoints, IEEE int. Conf. Comp. Vis., pp 2548–2555

  40. Li H, Luo W, Qiu X, Huang J (2017) Image forgery localization via integrating tampering possibility maps. IEEE Transactions on Information Forensics and Security 12(5):1240–1252

    Article  Google Scholar 

  41. Neubert T (2017) Face morphing detection: an approach based on image degradation analysis. International Workshop on Digital Watermarking, Springer, Cham

    Google Scholar 

  42. Obara Y, Niwa Y, Wada S (2017) Detection and Identification of Image Manipulation Based on Reversible Histogram Shift. Electronics and Communications in Japan 100.9:13–22

    Article  Google Scholar 

  43. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans on Pattern Analysis and Machine Intelligence 24(7):971–987

    Article  Google Scholar 

  44. Prakash CS et al (2018) Keypoint-based passive method for image manipulation detection. Cogent Engineering 5.1:1523346

    Article  Google Scholar 

  45. Rao, Y, Ni, J (2016). A deep learning approach to detection of splicing and copy-move forgeries in images. In: Proceedings of the IEEE International Workshop on Information Forensics and Security. Abu Dhabi, UAE, Dec 4–7

  46. Ren S, He K, Girshick R, Sun J (2016) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149

    Article  Google Scholar 

  47. Rublee E, Rabaud V, Konolige K,Bradski G (2011). ORB: an efficient alternative to SIFT or SURF, international conference on computer vision, Barcelona, Nov. 6-13, pp. 2564–257

  48. Seibold, Clemens, et al. (2017). “Detection of face morphing attacks by deep learning.” International Workshop on Digital Watermarking. Springer, Cham

  49. Singh D, Singh SK (2016) Effective self-embedding watermarking scheme for image tampered detection and localization with recovery capability. J Vis Commun Image Represent 38:775–789

    Article  Google Scholar 

  50. Sundaram Meenakshi and Nandini, C. n.d.. “Image retouching and it's detection - a survey” International Journal of Research in Engineering and Technology eISSN: 2319–1163 | pISSN: 2321–7308

  51. Sundaram A Meenakshi, and C Nandini (2017). “ASRD: Algorithm for Spliced Region Detection in Digital Image Forensics.” Computer Science On-line Conference. Springer, Cham

  52. Su B, Kaizhen Z (2012). Detection of Copy Forgery in Digital Images Based on LPP-SIFT., Int. Conf. Ind. Control Electron. Eng., pp. 1773–1776

  53. Saleh SQ, Hussain M, Muhammad G, Bebis G (2013) Evaluation of image forgery detection using multi-scale weber local descriptors. Proceedings International Symposium on Advances in Visual Computing, In, pp 416–424

    Google Scholar 

  54. Shi Z, Shen X, Kang H, Lv Y (2018) Image manipulation detection and localization based on the dual-domain convolutional neural networks. IEEE Access 6:76437–76453

    Article  Google Scholar 

  55. Toldo. R and A Fusiello (2008). “Robust multiple structures estimation with J-Linkage,” in Proc. Eur. Conf. Comput. Vision., Berlin, pp. 537547

  56. Vedaldi, A and B. Fulkerson (2008). “VLFeat: An open and portable library of computer vision algorithms,” http://www.vlfeat.org/

  57. Wu M-L, Fahn C-S, Chen Y-F (2017) Image-format-independent tampered image detection based on overlapping concurrent directional patterns and neural networks. Appl Intell 47(2):347–361

    Article  Google Scholar 

  58. Williams D, Hinton G (1986) Learning representations by back propagating errors. Nature 323:533–536

    Article  Google Scholar 

  59. Yang, Y, W Ren, Y Guo, R Wang and X Cao (2017). “Image deblurring via extreme channels prior,” in Procs. IEEE Int. Conf. Comp. Vision and Pattern Recognition (CVPR), Accepted

  60. Zhang G, Huang X (2005) Boosting local binary pattern (LBP)-based face recognition. Adv Biometr Person Authent 3338:179–186

    Article  Google Scholar 

  61. Zhang Y, Zhao C, Pi Y, Li S (2012). Revealing image splicing forgery using local binary patterns of DCT coefficients. In: Proceedings International Conference on Communications, Signal Processing, and Systems, pp. 181–189

  62. Zhang, Y, LL Win, J Goh, and VL Thing (2016). “Image region forgery detection: A deep learning approach,” in Proc. Singapore Cyber-Secur. Conf. (SG-CRC), pp. 111

  63. Zhang, Ying, and Vrizlynn LL Thing (2018). “A semi-feature learning approach for tampered region localization across multi-format images.” Multimedia Tools and Applications 1–26

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Anbu, T., Joe, M.M. & Murugeswari, G. A comprehensive survey of detecting tampered images and localization of the tampered region. Multimed Tools Appl 80, 2713–2751 (2021). https://doi.org/10.1007/s11042-020-09585-z

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