Multimedia Tools and Applications

, Volume 77, Issue 14, pp 18269–18293 | Cite as

Copy-move forgery detection based on convolutional kernel network

  • Yaqi Liu
  • Qingxiao Guan
  • Xianfeng Zhao


Conventional copy-move forgery detection methods mostly make use of hand-crafted features to conduct feature extraction and patch matching. However, the discriminative capability and the invariance to particular transformations of hand-crafted features are not good enough, which imposes restrictions on the performance of copy-move forgery detection. To solve this problem, we propose to utilize Convolutional Kernel Network to conduct copy-move forgery detection. Convolutional Kernel Network is a kind of data-driven local descriptor with the deep convolutional architecture. It can achieve competitive performance for its excellent discriminative capability. To well adapt to the condition of copy-move forgery detection, three significant improvements are made: First of all, our Convolutional Kernel Network is reconstructed for GPU. The GPU-based reconstruction results in high efficiency and makes it possible to apply to thousands of patches matching in copy-move forgery detection. Second, a segmentation-based keypoint distribution strategy is proposed to generate homogeneous distributed keypoints. Last but not least, an adaptive oversegmentation method is adopted. Experiments on the publicly available datasets are conducted to testify the state-of-the-art performance of the proposed method.


Copy-move forgery detection Convolutional kernel network Keypoint distribution strategy Adaptive oversegmentation Image forensics 



This work was supported by the NSFC under U1636102 and U1536105, and National Key Technology R&D Program under 2016YFB0801003 and 2016QY15Z2500.


  1. 1.
    Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S (2012) Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282CrossRefGoogle Scholar
  2. 2.
    Amerini I, Ballan L, Caldelli R, Bimbo A D, Serra G (2011) A sift-based forensic method for copycmove attack detection and transformation recovery. IEEE Trans Inf Forens Secur 6(3):1099–1110CrossRefGoogle Scholar
  3. 3.
    Amin R, Islam SH, Vijayakumar P, Khan MK, Chang V (2017) A robust and efficient bilinear pairing based mutual authentication and session key verification over insecure communication. Multimed Tools Appl 1–26Google Scholar
  4. 4.
    Amin R, Kumar N, Biswas G, Iqbal R, Chang V (2018) A light weight authentication protocol for iot-enabled devices in distributed cloud computing environment. Futur Gener Comput Syst 78:1005–1019CrossRefGoogle Scholar
  5. 5.
    Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916CrossRefGoogle Scholar
  6. 6.
    Ardizzone E, Bruno A, Mazzola G (2015) Copycmove forgery detection by matching triangles of keypoints. IEEE Trans Inf Forens Secur 10(10):2084–2094CrossRefGoogle Scholar
  7. 7.
    Bashar M, Noda K, Ohnishi N, Mori K (2010) Exploring duplicated regions in natural images. IEEE Trans Image ProcessGoogle Scholar
  8. 8.
    Bayar B, Stamm MC (2016) A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proceedings of the 4th ACM workshop on information hiding and multimedia security. ACM, pp 5–10Google Scholar
  9. 9.
    Borji A, Cheng MM, Jiang H, Li J (2014)Google Scholar
  10. 10.
    Chang V (2017) A cybernetics social cloud. J Syst Softw 124:195–211CrossRefGoogle Scholar
  11. 11.
    Chang V, Ramachandran M (2016) Towards achieving data security with the cloud computing adoption framework. IEEE Trans Serv Comput 9(1):138–151CrossRefGoogle Scholar
  12. 12.
    Chang V, Wills G (2016) A model to compare cloud and non-cloud storage of big data. Futur Gener Comput Syst 57:56–76CrossRefGoogle Scholar
  13. 13.
    Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Forens Secur 7(6):1841–1854CrossRefGoogle Scholar
  14. 14.
    Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copycmove forgery detection. IEEE Trans Inform Forens Secur 10(11):2284–2297CrossRefGoogle Scholar
  15. 15.
    Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low-and high-dimensional approaches. IEEE Trans Syst Man Cybern Syst 43(4):996–1002CrossRefGoogle Scholar
  16. 16.
    Ferreira A, Felipussi SC, Alfaro C, Fonseca P, Vargas-Munoz JE, dos Santos JA, Rocha A (2016) Behavior knowledge space-based fusion for copycmove forgery detection. IEEE Trans Image Process 25(10):4729–4742MathSciNetCrossRefGoogle Scholar
  17. 17.
    Fischer P, Dosovitskiy A, Brox T (2014) Descriptor matching with convolutional neural networks: a comparison to sift. arXiv preprint arXiv:1405.5769
  18. 18.
    Fridrich AJ, Soukal BD, Lukáṡ AJ (2003) Detection of copy-move forgery in digital images. In: Proceedings of digital forensic research workshop. CiteseerGoogle Scholar
  19. 19.
    He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. arXiv preprint arXiv:1603.05027
  20. 20.
    Jegou H, Perronnin F, Douze M, Sanchez J, Perez P, Schmid C (2012) Aggregating local image descriptors into compact codes. IEEE Trans Pattern Anal Mach Intell 34(9):1704–1716CrossRefGoogle Scholar
  21. 21.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Proceedings of the advances in neural information processing systems, pp 1097–1105Google Scholar
  22. 22.
    Li Y (2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forens Sci Int 224(1):59–67CrossRefGoogle Scholar
  23. 23.
    Li G, Wu Q, Tu D, Sun S (2007) A sorted neighborhood approach for detecting duplicated regions in image forgeries based on dwt and svd. In: 2007 IEEE International conference on multimedia and expo. IEEE, pp 1750–1753Google Scholar
  24. 24.
    Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inform Forens Secur 10(3):507–518CrossRefGoogle Scholar
  25. 25.
    Li L, Li S, Zhu H, Chu S C, Roddick JF, Pan JS (2013) An efficient scheme for detecting copy-move forged images by local binary patterns. J Inform Hiding Multimed Signal Process 4(1):46–56Google Scholar
  26. 26.
    Liu Y, Zhang X, Cui J, Wu C, Aghajan H, Zha H (2010) Visual analysis of child-adult interactive behaviors in video sequences. In: 2010 16th International conference on virtual systems and multimedia (VSMM). IEEE, pp 26–33Google Scholar
  27. 27.
    Liu Y, Cui J, Zhao H, Zha H (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. In: 2012 21st International conference on pattern recognition (ICPR). IEEE, pp 898–901Google Scholar
  28. 28.
    Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2activity: recognizing complex activities from sensor data. In: IJCAI, pp 1617–1623Google Scholar
  29. 29.
    Liu Y, Cai Q, Zhu X, Cao J, Li H (2015) Saliency detection using two-stage scoring. In: 2015 IEEE International conference on image processing (ICIP). IEEE, pp 4062–4066Google Scholar
  30. 30.
    Liu L, Cheng L, Liu Y, Jia Y, Rosenblum DS (2016) Recognizing complex activities by a probabilistic interval-based model. In: AAAI, vol 30, pp 1266–1272Google Scholar
  31. 31.
    Liu Y, Liang Y, Liu S, Rosenblum DS, Zheng Y (2016) Predicting urban water quality with ubiquitous data. arXiv preprint arXiv:1610.09462
  32. 32.
    Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115CrossRefGoogle Scholar
  33. 33.
    Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune teller: predicting your career path. In: AAAI, pp 201–207Google Scholar
  34. 34.
    Liu Y, Zheng Y, Liang Y, Liu S, Rosenblum DS (2016) Urban water quality prediction based on multi-task multi-view learningGoogle Scholar
  35. 35.
    Liu Y, Guan Q, Zhao X, Cao Y (2017) Image forgery localization based on multi-scale convolutional neural networks. arXiv preprint arXiv:1706.07842
  36. 36.
    Liu Y, Zhang X, Zhu X, Guan Q, Zhao X (2017) Listnet-based object proposals ranking. NeurocomputingGoogle Scholar
  37. 37.
    Lu Y, Wei Y, Liu L, Zhong J, Sun L, Liu Y (2017) Towards unsupervised physical activity recognition using smartphone accelerometers. Multimed Tools Appl 76(8):10,701–10,719CrossRefGoogle Scholar
  38. 38.
    Mahdian B, Saic S (2007) Detection of copy–move forgery using a method based on blur moment invariants. Forens Sci Int 171(2):180–189CrossRefGoogle Scholar
  39. 39.
    Mairal J (2016) End-to-end kernel learning with supervised convolutional kernel networks. In: Proceedings of the advances in neural information processing systemsGoogle Scholar
  40. 40.
    Mairal J, Koniusz P, Harchaoui Z, Schmid C (2014) Convolutional kernel networks. In: Proceedings of the advances in neural information processing systems, pp 2627–2635Google Scholar
  41. 41.
    Maninis KK, Pont-Tuset J, Arbeláez P, Van Gool L (2016) Convolutional oriented boundaries. In: European conference on computer vision. Springer, pp 580–596Google Scholar
  42. 42.
    Mostajabi M, Yadollahpour P, Shakhnarovich G (2015) Feedforward semantic segmentation with zoom-out features. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3376–3385Google Scholar
  43. 43.
    Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inform Forens Secur 5(4):857–867CrossRefGoogle Scholar
  44. 44.
    Paulin M, Douze M, Harchaoui Z, Mairal J, Perronin F, Schmid C (2015) Local convolutional features with unsupervised training for image retrieval. In: Proceedings of the international conference on computer vision. IEEE, pp 91–99Google Scholar
  45. 45.
    Paulin M, Mairal J, Douze M, Harchaoui Z, Perronnin F, Schmid C (2017) Convolutional patch representations for image retrieval: an unsupervised approach. Int J Comput Vis 121(1):149–168CrossRefGoogle Scholar
  46. 46.
    Pont-Tuset J, Arbelaez P, Barron JT, Marques F, Malik J (2017) Multiscale combinatorial grouping for image segmentation and object proposal generation. IEEE Trans Pattern Anal Mach Intell 39(1):128–140CrossRefGoogle Scholar
  47. 47.
    Popescu A, Farid H (2004) Exposing digital forgeries by detecting duplicated image region [technical report]. 2004-515. Hanover, Department of Computer Science, Dartmouth College, USA, p 32Google Scholar
  48. 48.
    Preoṫiuc-Pietro D, Liu Y, Hopkins D, Ungar L (2017) Beyond binary labels: political ideology prediction of twitter users. In: Proceedings of the 55th annual meeting of the association for computational linguistics (volume 1: Long Papers), vol 1, pp 729–740Google Scholar
  49. 49.
    Pun CM, Yuan XC, Bi XL (2015) Image forgery detection using adaptive oversegmentation and feature point matching. IEEE Trans Inform Forens Secur 10(8):1705–1716CrossRefGoogle Scholar
  50. 50.
    Rao Y, Ni J (2016) A deep learning approach to detection of splicing and copy-move forgeries in images. In: IEEE International workshop on information forensics and security (WIFS). IEEE, pp 1–6Google Scholar
  51. 51.
    Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Proceedings of the advances in neural information processing systems, pp 91–99Google Scholar
  52. 52.
    Ryu SJ, Kirchner M, Lee MJ, Lee HK (2013) Rotation invariant localization of duplicated image regions based on zernike moments. IEEE Trans Inform Forens Secur 8(8):1355–1370CrossRefGoogle Scholar
  53. 53.
    Shivakumar B, Baboo LDSS (2011) Detection of region duplication forgery in digital images using surf. IJCSI Int J Comput Sci Issues 8(4)Google Scholar
  54. 54.
    Silva E, Carvalho T, Ferreira A, Rocha A (2015) Going deeper into copy-move forgery detection: Exploring image telltales via multi-scale analysis and voting processes. J Vis Commun Image Represent 29:16–32CrossRefGoogle Scholar
  55. 55.
    Simo-Serra E, Trulls E, Ferraz L, Kokkinos I, Moreno-Noguer F (2014) Fracking deep convolutional image descriptors. arXiv preprint arXiv:1412.6537
  56. 56.
    Simo-Serra E, Trulls E, Ferraz L, Kokkinos I, Fua P, Moreno-Noguer F (2015) Discriminative learning of deep convolutional feature point descriptor. In: Proceedings of the IEEE International conference on computer vision, pp 118–126Google Scholar
  57. 57.
    Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.155
  58. 58.
    Smeulders AW, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380CrossRefGoogle Scholar
  59. 59.
    Sohal AS, Sandhu R, Sood SK, Chang V (2017) A cybersecurity framework to identify malicious edge device in fog computing and cloud-of-things environments. Comput SecurGoogle Scholar
  60. 60.
    Sun G, Xie Y, Liao D, Yu H, Chang V (2017) User-defined privacy location-sharing system in mobile online social networks. J Netw Comput Appl 86:34–45CrossRefGoogle Scholar
  61. 61.
    Tralic D, Zupancic I, Grgic S, Grgic M (2013) Comofodnew database for copy-move forgery detection. In: ELMAR, 2013 55th international symposium. IEEE, pp 49–54Google Scholar
  62. 62.
    Xie S, Tu Z (2015) Holistically-nested edge detection. In: Proceedings of the IEEE international conference on computer vision, pp 1395–1403Google Scholar
  63. 63.
    Yang B, Sun X, Guo H, Xia Z, Chen X (2017) A copy-move forgery detection method based on cmfd-sift. Multimed Tools Appl 1–19Google Scholar
  64. 64.
    Yang Y, Zheng X, Chang V, Tang C (2017) Semantic keyword searchable proxy re-encryption for postquantum secure cloud storage. Concurr Comput Pract Exper 29(19)Google Scholar
  65. 65.
    Yang Y, Zheng X, Chang V, Ye S, Tang C (2017) Lattice assumption based fuzzy information retrieval scheme support multi-user for secure multimedia cloud. Multimed Tools Appl 1–15Google Scholar
  66. 66.
    Yang Y, Zheng X, Liu X, Zhong S, Chang V (2017) Cross-domain dynamic anonymous authenticated group key management with symptom-matching for e-health social system. Fut Gen Comput SystemsGoogle Scholar
  67. 67.
    Zandi M, Mahmoudi-Aznaveh A, Talebpour A (2016) Iterative copy-move forgery detection based on a new interest point detector. IEEE Trans Inform Forens Secur 11(11):2499–2512CrossRefGoogle Scholar
  68. 68.
    Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Proceedings of the European conference on computer vision. Springer, pp 818–833Google Scholar
  69. 69.
    Zhang XY, Wang S, Yun X (2015) Bidirectional active learning: a two-way exploration into unlabeled and labeled data set. IEEE Trans Neural Netw Learn Syst 26(12):3034–3044MathSciNetCrossRefGoogle Scholar
  70. 70.
    Zhang XY, Wang S, Zhu X, Yun X, Wu G, Wang Y (2015) Update vs. upgrade: modeling with indeterminate multi-class active learning. Neurocomputing 162:163–170CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.State Key Laboratory of Information Security, Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  2. 2.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina

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