Skip to main content
SpringerLink
Log in

Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Image forgery detection remains a challenging problem. For the most common copy-move forgery detection, the robustness and accuracy of existing methods can still be further improved. To the best of our knowledge, we are the first to propose an image copy-move forgery passive detection method by combining the improved pulse coupled neural network (PCNN) and the self-selected sub-images. Our method has the following steps: First, contour detection is performed on the input color image, and bounding boxes are drawn to frame the contours to form suspected forgery sub-images. Second, by improving PCNN to perform feature extraction of sub-images, the feature invariance of rotation, scaling, noise adding, and so on can be achieved. Finally, the dual feature matching is used to match the features and locate the forgery regions. What’s more, the self-selected sub-images can quickly obtain suspected forgery sub-images and lessen the workload of feature extraction, and the improved PCNN can extract image features with high robustness. Through experiments on the standard image forgery datasets CoMoFoD and CASIA, it is effectively verified that the robustness score and accuracy of proposed method are much higher than the current best method, which is a more efficient image copy-move forgery passive detection method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Ferreira A, Felipussi S C, Alfaro C, Fonseca P, Vargas-Muñoz J E, Santos J A, Rocha A. Behavior knowledge space-based fusion for copy-move forgery detection. IEEE Transactions on Image Processing, 2016, 25(10): 4729–4742

    Article  MathSciNet  MATH  Google Scholar 

  3. Zhao J, Bi X. Image tampering and forensics algorithm based on local perception hash. Computer and Digital Engineering, 2019, 47(11): 2880–2883

    Google Scholar 

  4. Yan P, Su L, Shao H, Wu D. Image forgery detection based on local luminance order of multiple support regions. Computer Application, 2019, 39(9): 2707–2711

    Google Scholar 

  5. Liu S, Huang Z. Efficient image hashing with geometric invariant vector distance for copy detection. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2019, 15(4): 1–22

    Google Scholar 

  6. Bi X, Pun C M. Fast reflective offset-guided searching method for copy-move forgery detection. Information Sciences, 2017, 418: 531–545

    Article  Google Scholar 

  7. Li Y, Zhou J. Fast and effective image copymove forgery detection via hierarchical feature point matching. IEEE Transactions on Information Forensics and Security, 2018, 14(5): 1307–1322

    Article  Google Scholar 

  8. Kim K S, Lee M J, Lee J W, Oh T W, Lee H Y. Region-based tampering detection and recovery using homogeneity analysis in quality-sensitive imaging. Computer Vision and Image Understanding, 2011, 115(9): 1308–1323

    Article  Google Scholar 

  9. Bappy J H, Simons C, Nataraj L, Manjunath B S, Roy-Chowdhury A K. Hybrid lstm and encoder-decoder architecture for detection of image forgeries. IEEE Transactions on Image Processing, 2019, 28(7): 3286–3300

    Article  MathSciNet  MATH  Google Scholar 

  10. Vega E A A, Fernández E G, Orozco A L S, Villalba L J G. Passive image forgery detection based on the demosaicing algorithm and JPEG compression. IEEE Access, 2020, 8: 11815–11823

    Article  Google Scholar 

  11. Kaur H, Jindal N. Image and video forensics: a critical survey. Wireless Personal Communications, 2020, 112(2): 1281–1300

    Article  Google Scholar 

  12. Xue F, Lu W, Ren H, Xiao H, Zhang Q, Liu X. Forensics of visual privacy protection in digital images. Multimedia Tools and Applications, 2020, 79(17–18): 12427–12445

    Article  Google Scholar 

  13. Lin X, Li J H, Wang S L, Cheng F, Huang X S. Recent advances in passive digital image security forensics: a brief review. Engineering, 2018, 4(1): 29–39

    Article  Google Scholar 

  14. Chen C, Ni J, Shen Z, Shi Y Q. Blind forensics of successive geometric transformations in digital images using spectral method: theory and applications. IEEE Transactions on Image Processing, 2017, 26(6): 2811–2824

    Article  MathSciNet  MATH  Google Scholar 

  15. Ren Z, Sun Q, Wu B, Zhang X, Yan W. Learning latent low-rank and sparse embedding for robust image feature extraction. IEEE Transactions on Image Processing, 2019, 29(1): 2094–2107

    MATH  Google Scholar 

  16. Zhang D, Yi L, Tang H, Zhang Y, Xu H. Multi-scale microstructure binary pattern extraction and learning for image representation. IET Image Processing, 2019, 13(13): 2507–2515

    Article  Google Scholar 

  17. Shi J, Wang X. A local feature with multiple line descriptors and its speeded-up matching algorithm. Computer Vision and Image Understanding, 2017, 162: 57–70

    Article  Google Scholar 

  18. Hu W, Fan Y, Xing J, Sun L, Cai Z, Maybank S. Deep constrained siamese hash coding network and load-balanced locality-sensitive hashing for near duplicate image detection. IEEE Transactions on Image Processing, 2018, 27(9): 4452–4464

    Article  MathSciNet  MATH  Google Scholar 

  19. Gu X. Feature extraction using unit-linking pulse coupled neural network and its applications. Neural Processing Letters, 2008, 27(1): 25–41

    Article  Google Scholar 

  20. Medathati N V K, Neumann H, Masson G S, Kornprobst P. Bio-inspired computer vision: towards a synergistic approach of artificial and biological vision. Computer Vision and Image Understanding, 2016, 150: 1–30

    Article  Google Scholar 

  21. Eckhorn R, Reitboeck H J, Arndt M T, Dicke P. Feature linking via synchronization among distributed assemblies: simulations of results from cat visual cortex. Neural Computation, 1990, 2(3): 293–307

    Article  Google Scholar 

  22. Lindblad T, Kinser J M, Taylor J G. Image Processing Using Pulse-Coupled Neural Networks. Heidelberg: Springer, 2005

    MATH  Google Scholar 

  23. Thyagharajan K K, Kalaiarasi G. Pulse coupled neural network based near-duplicate detection of images (PCNN-NDD). Advances Electrical Computer Engineering, 2018, 18(3): 87–97

    Article  Google Scholar 

  24. Wang C, Zhang Z, Li Q, Zhou X. An image copy-move forgery detection method based on SURF and PCET. IEEE Access, 2019, 7: 170032–170047

    Article  Google Scholar 

  25. Chen B, Yu M, Su Q, Shim H J, Shi Y Q. Fractional quaternion zernike moments for robust color image copy-move forgery detection. IEEE Access, 2018, 6: 56637–56646

    Article  Google Scholar 

  26. Tian X, Zhou G, Xu M. Image copy-move forgery detection algorithm based on ORB and novel similarity metric. IET Image Processing, 2020, 14(10): 2092–2100

    Article  Google Scholar 

  27. Zhu Y, Shen X, Chen H. Copy-move forgery detection based on scaled ORB. Multimedia Tools and Applications, 2016, 75(6): 3221–3233

    Article  Google Scholar 

  28. Prakash C S, Panzade P P, Om H, Maheshkar S. Detection of copy-move forgery using AKAZE and SIFT keypoint extraction. Multimedia Tools and Applications, 2019, 78(16): 23535–23558

    Article  Google Scholar 

  29. Paul K H, Akshatha K R, Karunakar A K, Seshadri S. SURF based copy move forgery detection using kNN mapping. In: Proceedings of Science and Information Conference. 2019, 234–245

  30. Chen C C, Lu W Y, Chou C H. Rotational copy-move forgery detection using SIFT and region growing strategies. Multimedia Tools and Applications, 2019, 78(13): 18293–18308

    Article  Google Scholar 

  31. Rajalakshmi C, Alex M G, Balasubramanian R. Copy move forgery detection using key point localized super pixel based on texture features. Computer Optics, 2019, 43(2): 270–276

    Article  Google Scholar 

  32. Mahmood T, Nawaz T, Mehmood Z, Khan Z, Shah M, Ashraf R. Forensic analysis of copy-move forgery in digital images using the stationary wavelets. In: Proceedings of the 6th International Conference on Innovative Computing Technology. 2016, 578–583

  33. Alkawaz M H, Sulong G, Saba T, Rehman A. Detection of copy-move image forgery based on discrete cosine transform. Neural Computing and Applications, 2018, 30(1): 183–192

    Article  Google Scholar 

  34. Meena K B, Tyagi V. A copy-move image forgery detection technique based on Gaussian-Hermite moments. Multimedia Tools and Applications, 2019, 78(23): 33505–33526

    Article  Google Scholar 

  35. Thajeel S A, Mahmood A S, Humood W R, Sulong G. Detection copy-move forgery in image via quaternion polar harmonic transforms. KSII Transactions on Internet and Information Systems (TIIS), 2019, 13(8): 4005–4025

    Google Scholar 

  36. Rajkumar R, Roy S, Singh K M. A robust and forensic transform for copy move digital image forgery detection based on dense depth block matching. The Imaging Science Journal, 2019, 67(6): 343–357

    Article  Google Scholar 

  37. Liu Y, Guan Q, Zhao X. Copy-move forgery detection based on convolutional kernel network. Multimedia Tools and Applications, 2018, 77(14): 18269–18293

    Article  Google Scholar 

  38. Wu Y, Abd-Almageed W, Natarajan P. Image copy-move forgery detection via an end-to-end deep neural network. In: Proceedings of 2018 IEEE Winter Conference on Applications of Computer Vision. 2018, 1907–1915

  39. Wu Y, Abd-Almageed W, Natarajan P. Busternet: detecting copy-move image forgery with source/target localization. In: Proceedings of the European Conference on Computer Vision. 2018, 168–184

  40. Zhong J L, Pun C M. An end-to-end dense-inceptionnet for image copy-move forgery detection. IEEE Transactions on Information Forensics and Security, 2019, 15: 2134–2146

    Article  Google Scholar 

  41. Aymaz S, Aymaz Ş, Ulutaş. G. Detection of copy move forgery using legendre moments. In: Proceedings of the 24th Signal Processing and Communication Application Conference. 2016, 1125–1128

  42. Mammone N, Ieracitano C, Adeli H, Bramanti A, Morabito F C. Permutation Jaccard distance-based hierarchical clustering to estimate EEG network density modifications in MCI subjects. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(10): 5122–5135

    Article  Google Scholar 

  43. Ma Y, Wang Z, Wu C. Feature extraction from noisy image using PCNN. In: Proceedings of 2006 IEEE International Conference on Information Acquisition. 2006, 808–813

  44. Tralic D, Zupancic I, Grgic S, Grgic M. CoMoFoD-New database for copy-move forgery detection. In: Proceedings of ELMAR-2013. 2013, 49–54

  45. Dong J, Wang W, Tan T. CASIA image tampering detection evaluation database. In: Proceedings of 2013 IEEE China Summit and International Conference on Signal and Information Processing. 2013, 422–426

  46. Powers D M. Evaluation: from precision, recall and Fmeasure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2011, 2(1): 37–63

    MathSciNet  Google Scholar 

  47. Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E. An evaluation of popular copy-move forgery detection approaches. IEEE Transactions on Information Forensics and Security, 2012, 7(6): 1841–1854

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grants Nos 61772327, 61532021) and Project of Electric Power Research Institute of State Grid Gansu Electric Power Company (H2019-275).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, ShangHai University of Electric Power, Shanghai, 200090, China

    Guoshuai Zhou & Xiuxia Tian

  2. School of Data Science and Engineering, East China Normal University, Shanghai, 200062, China

    Aoying Zhou

Authors
  1. Guoshuai Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiuxia Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aoying Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiuxia Tian.

Additional information

Guoshuai Zhou is currently a second year graduate student pursuing a MS degree in power information technology at Shanghai University of Electric Power, China. His main research interests are image and signal processing, and image forgery detection.

Xiuxia Tian received the MS degree in applied cryptography-based information security from Shanghai Jiaotong University, China in 2005, and the PhD degree in database security and privacy preserving in cloud computing from Fudan University, China in 2011. She is a professor of the School of Computer Science and Technology, Shanghai University of Electric Power, China and a visiting scholar in the UC Berkeley, USA from 2013 to 2015. Her main interests are digital image forgery detection, database security, privacy preserving (large data and cloud computing), secure machine learning and security computing for the benefit of power users. She has been presiding over and participating in over 10 scientific research programs.

Aoying Zhou got his master and bachelor degree in Computer Science from Sichuan University, China in 1988 and 1985, respectively, and he won his PhD degree from Fudan University, China in 1993. He is a Vice President of East China Normal University (ECNU), Dean of School of Data Science and Engineering (DaSE), and a professor on Computer Science and Data Science. His research interests include Web data management, data management for dataintensive computing, in-memory cluster computing. He is the winner of the National Science Fund for Distinguished Young Scholars supported by National Natural Science Foundation of China (NSFC).

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, G., Tian, X. & Zhou, A. Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images. Front. Comput. Sci. 16, 164705 (2022). https://doi.org/10.1007/s11704-021-0450-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-021-0450-5

Keywords

Search

Navigation