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Image splicing detection using mask-RCNN

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Abstract

Digital images have become a dominant source of information and means of communication in our society. However, they can easily be altered using readily available image editing tools. In this paper, we propose a new blind image forgery detection technique which employs a new backbone architecture for deep learning which is called ResNet-conv. ResNet-conv is obtained by replacing the feature pyramid network in ResNet-FPN with a set of convolutional layers. This new backbone is used to generate the initial feature map which is then to train the Mask-RCNN to generate masks for spliced regions in forged images. The proposed network is specifically designed to learn discriminative artifacts from tampered regions. Two different ResNet architectures are considered, namely ResNet-50 and ResNet-101. The ImageNet, He_normal, and Xavier_normal initialization techniques are employed and compared based on convergence. To train a robust model for this architecture, several post-processing techniques are applied to the input images. The proposed network is trained and evaluated using a computer-generated image splicing dataset and found to be more efficient than other techniques.

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References

  1. Qazi, T., et al.: Survey on blind image forgery detection. IET Image Process. 7(7), 660–670 (2013)

    Article  Google Scholar 

  2. Mahdian, B., Saic, S.: Blind methods for detecting image fakery. In: Proceedings of the IEEE International Carnahan Conference on Security Technology. Prague, Czech Republic, Oct 13–16 (2008)

  3. Shivakumar, B., Baboo, L.D.S.S.: Detecting copy-move forgery in digital images: a survey and analysis of current methods. Glob. J. Comput. Sci. Technol. 10(7), 61–65 (2010)

    Google Scholar 

  4. Lin, C., Chen, C., Chang, Y.: An efficiency enhanced cluster expanding block algorithm for copy-move forgery detection. In: Proceedings of the IEEE International Conference on Intelligent Networking and Collaborative Systems. Taipei, Taiwan, Sep 2–4 (2015)

  5. Ardizzone, E., Bruno, A., Mazzola, G.: Copy-move forgery detection by matching triangles of keypoints. IEEE Trans. Inf. Forensics Secur. 10(10), 2084–2094 (2015)

    Article  Google Scholar 

  6. Cozzolino, D., Gragnaniello, D., Verdoliva, L.: Image forgery detection based on the fusion of machine learning and block-matching methods. arXiv preprint arXiv:1311.6934 (2013)

  7. Bharati, A., et al.: Detecting facial retouching using supervised deep learning. IEEE Trans. Inf. Forensics Secur. 11(9), 1903–1913 (2016)

    Article  Google Scholar 

  8. Shi, Y.Q., Chen, C., Chen, W.: A natural image model approach to splicing detection. In: Proceedings of the Workshop on Multimedia & Security. Dallas, TX, USA, pp. 51–62, Sep 20–21 (2007)

  9. He, Z., et al.: Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recognit. 45(12), 4292–4299 (2012)

    Article  Google Scholar 

  10. Mushtaq, S., Mir, A.H.: Novel method for image splicing detection. In: Proceedings of the IEEE International Conference on Advances in Computing, Commun. and Informatics. New Delhi, India, Sep 24–27 (2014)

  11. Dong, J., et al.: Run-length and edge statistics based approach for image splicing detection. In: Proceedings of the International Workshop on Digital Watermarking. Busan, Korea, Nov 10–12 (2008)

  12. He, Z., Lu, W., Sun, W.: Improved run length based detection of digital image splicing. In: Proceedings of the International Workshop on Digital Watermarking. Lecture Notes in Computer Science, vol. 7128. Springer, Berlin, Atlantic City, NJ, USA, Oct. 23–26 (2011)

  13. Lee, H., Ekanadham, C., Ng, A.Y.: Sparse deep belief net model for visual area V2. In: Proceedings of the Advances in Neural Information Processing Systems Conference. Vancouver, BC, Canada, Dec 3–5 (2007)

  14. Hugo, L., et al.: Exploring strategies for training deep neural networks. J. Mach. Learn. Res. 10, 1–40 (2009)

    MATH  Google Scholar 

  15. LeCun, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  16. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the Advances in Neural Information Processing Systems Conference. Lake Tahoe, NV, USA, Dec 3–8 (2012)

  17. Swietojanski, P., Ghoshal, A., Renals, S.: Convolutional neural networks for distant speech recognition. IEEE Signal Process. Lett. 21(9), 1120–1124 (2014)

    Article  Google Scholar 

  18. Tuama, A., Comby, F., Chaumont, M.: Camera model identification with the use of deep convolutional neural networks. In: Proceedings of the IEEE International Workshop on Information Forensics and Security. Abu Dhabi, UAE, Dec 4–7 (2016)

  19. Baroffio, L., et al.: Camera identification with deep convolutional networks (2016)

  20. Rao, Y., Ni, J.: 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 (2016)

  21. Rota, P., et al.: Bad teacher or unruly student: can deep learning say something in image forensics analysis? In: Proceedings of the IEEE International Conference on Pattern Recognition. Cancun, Mexico, Dec 4–8 (2016)

  22. Bayar, B., Stamm, M.C.: A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proceedings of the ACM Workshop on Information Hiding and Multimedia Security. Galicia, Spain, Jun 20–22 (2016)

  23. Wang, Q., Zhang, R.: Double JPEG compression forensics based on a convolutional neural network. EURASIP J. Inf. Secur. 2016, 23 (2016)

    Article  Google Scholar 

  24. Ying, Z., et al.: Image region forgery detection: a deep learning approach. In: Proceedings of the Singapore Cyber-Security Conference. Singapore, Jan 14–15 (2016)

  25. Zhang, Z., et al.: Boundary-based image forgery detection by fast shallow CNN. In: Proceedings of the IEEE International Conference on Pattern Recognition. Beijing, China, Aug 20–24 (2018)

  26. Ouyang, J., Liu, Y., Liao, M.: Copy-move forgery detection based on deep learning. In: Proceedings of the IEEE International Congress on Image and Signal Processing, BioMedical Engineering and Informatics. Shanghai, China, Oct 14–16 (2017)

  27. Lin, T.Y., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA, Jul 22–25 (2017)

  28. He, K., et al.: 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 (2016)

  29. Deng, J., et al.: Imagenet: a large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA, Jun 20–25 (2009)

  30. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the International Conference on Artificial Intelligence and Statistics. Sardinia, Italy, May 13–15 (2010)

  31. He, K., et al.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile, Dec 13–16 (2015)

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

  33. Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: Proceedings of the European Conference on Computer Vision. Zurich, Switzerland, Sep 6–12 (2014)

  34. Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2016)

    Article  Google Scholar 

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

  36. Meena, K., Tyagi, V.: Image forgery detection: survey and future directions. In: Shukla, R., Agrawal, J., Sharma, S., Singh Tomer, G. (eds.) Data, engineering and applications, pp. 163–194. Springer, Singapore (2019)

    Chapter  Google Scholar 

  37. Braxmeier, J.: Stunning free images & royalty free stock. https://pixabay.com. Cited 09-Jun-2019 (2018)

  38. Matterport Inc.: Mask-RCNN. https://github.com/matterport/Mask_RCNN. Cited 09-Jun-2019 (2017)

  39. Qian, N.: On the momentum term in gradient descent learning algorithm. Neural Netw. 12(1), 145–151 (1999)

    Article  MathSciNet  Google Scholar 

  40. Keras-team: Keras. https://github.com/fchollet/keras. Cited 09-Jun-2019 (2015)

  41. Casella, G., Berger, R.L.: Statistical Inference, 2nd edn. Duxbury Press, Pacific Grove (2002)

    MATH  Google Scholar 

  42. Sakamoto, H.: On the distributions of the product and the quotient of the independent and uniformly distributed random variables. Tohoku Math. J., First Stage 49, 243–260 (1943)

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

The authors would like to thank Radwa Hammad for her comments and advice that greatly improve the manuscript. They would also like to thank the anonymous reviewers for their insightful suggestions and comments.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Victoria, Victoria, Canada

    Belal Ahmed & T. Aaron Gulliver

  2. Department of Computer Science and Engineering, University of Alaska Anchorage, Anchorage, USA

    Saif alZahir

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  1. Belal Ahmed
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  2. T. Aaron Gulliver
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  3. Saif alZahir
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Correspondence to Belal Ahmed.

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Ahmed, B., Gulliver, T.A. & alZahir, S. Image splicing detection using mask-RCNN. SIViP 14, 1035–1042 (2020). https://doi.org/10.1007/s11760-020-01636-0

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  • DOI: https://doi.org/10.1007/s11760-020-01636-0

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