ICTACT Journal on Image and Video Processing

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Transfer Learning Approach for Splicing and Copy-Move Image Tampering Detection


Affiliations
1 Department of Electronics and Telecommunication Engineering, Thadomal Shahani Engineering College, India
     

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Image authentication before using in any security critical applications has become necessary as the image editing tools are increasing and are handy to use in today's world. Images could be tampered in different ways, but a universal method is required to detect it. Deep learning has gained its importance because of its promising performance in many applications. In this paper a new framework for image tampering detection using Error Level Analysis (ELA) and Convolutional Neural Network (CNN) with transfer learning approach is proposed. In this method, the images are pre-processed using ELA to highlight the tampered region and are used to fine tune the entire model. Six different pre-trained models are used in the proposed framework to compare the performance in classifying the tampered and authentic images. The complexity and processing time of the proposed method is low with respect to most of the existing methods as the images are not divided into patches. The performance of the model obtained is also considerably good with an accuracy of 97.58% with Residual Network 50(ResNet50).

Keywords

Tampering Detection, Transfer Learning, Copy-Move, Splicing.
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  • J. Dong, W. Wei and T. Tieniu, “Casia Image Tampering Detection Evaluation Database”, Proceedings of IEEE China Summit and International Conference on Signal and Information Processing, pp. 1-8, 2013.

  • I. Amerini, L. Ballan, R. Caldelli, A.D. Bimbo and G. Serra, “A Sift-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery”, IEEE Transactions on Information Forensics and Security, Vol. 6, No. 3, pp. 1099-1110, 2011.

  • I. Amerini, L. Ballan, R. Caldelli, D.B. Alberto, D.T. Luca and S. Giuseppe, “Copy-Move Forgery Detection and Localization by Means of Robust Clustering with J-Linkage”, Signal Processing: Image Communication, Vol. 28, No. 6, pp. 659-669, 2013.

  • S.J. Ryu, M.J. Lee and H.K. Lee, “Detection of Copy-Rotate-Move Forgery using Zernike Moments”, Information Hiding, Vol. 6387, pp 51-65, 2010.

  • X. Bo, W. Junwen, L. Guangjie and D. Yuewei, “Image Copy-Move Forgery Detection based on Surf”, Proceedings of International Conference on Multimedia Information Networking and Security, pp. 889-892, 2010.

  • B.L. Shivakumar and S. Baboo, “Detection of Region Duplication Forgery in Digital Images using Surf”, Proceedings of International Conference on Multimedia Information Networking and Security, pp. 889-892, 2010.

  • J. Li, X. Li, B. Yang and X. Sun, “Segmentation-Based Image Copy-Move Forgery Detection Scheme”, IEEE Transactions on Information Forensics and Security, Vol. 10, No. 3, pp. 507-518, 2014.

  • Y. Li, “Image Copy-Move Forgery Detection based on Polar Cosine Transform and Approximate Nearest Neighbor Searching”, Forensic Science, Vol. 224, No. 1-3, pp. 59-67, 2013.

  • G. Lynch, Y.S. Frank and M.L. Hong Yuan, “An Efficient Expanding Block Algorithm for Image Copy-Move Forgery Detection”, Information Sciences, Vol. 239, pp. 253-265, 2013.

  • Z. He, W. Lu, W. Sun and J. Huang, “Digital Image Splicing Detection based on Markov Features in DCT and DWT Domain”, Pattern Recognition, Vol. 45, No. 12, pp. 4292-4299, 2012.

  • C. Pun, L. Bo and Y. Xiao-Chen, “Multi-Scale Noise Estimation for Image Splicing Forgery Detection”, Journal of Visual Communication and Image Representation, Vol. 38, pp. 195-206, 2016.

  • D. Cozzolino, G. Poggi and L. Verdoliva, “Splicebuster: A New Blind Image Splicing Detector,” Proceedings of IEEE Workshop on Information Forensics and Security, pp. 1-6, 2015.

  • S.D. Lin and W. Tszan, “An Integrated Technique for Splicing and Copy-Move Forgery Image Detection”, Proceedings of International Congress on Image and Signal Processing, pp. 1-8, 2011.

  • W. Fan, W. Kai and C. François, “General-Purpose Image Forensics using Patch Likelihood under Image Statistical Models”, Proceedings of IEEE International Workshop on Information Forensics and Security, pp. 1-8, 2015.

  • M.M. Islam, J. Kamruzzaman, G. Karmakar, M. Murshed and G. Kahandawa, “Passive Detection of Splicing and Copy-Move Attacks in Image Forgery”, Proceedings of International Conference on Neural Information Processing, pp. 555-567, 2018.

  • B. Bayar and M.C. Stamm, “A Deep Learning Approach to Universal Image Manipulation Detection using a New Convolutional Layer”, Proceedings of ACM Workshop on Information Hiding and Multimedia Security, pp. 5-10, 2016.

  • Y. Zhang, J. Goh, L.L. Win and V.L. Thing, “Image Region Forgery Detection: A Deep Learning Approach”, Proceedings of International Conference on Information Technology, pp. 1-11, 2016.

  • X. Qiu, H. Li, W. Luo and J. Huang, “A Universal Image Forensic Strategy based on Steganalytic Model”, Proceedings of ACM Workshop on Information Hiding and Multimedia Security, pp. 165-170, 2016.

  • S. Akcay, M.E. Kundegorski, C.G. Willcocks and T.P. Breckon, “Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-Ray Baggage Security Imagery”, IEEE Transactions on Information Forensics and Security, Vol. 13, No. 9, pp. 2203-2215, 2018.

  • Y. Rao and J Ni, “A Deep Learning Approach to Detection of Splicing and Copy-Move Forgeries in Images”, Proceedings of IEEE International Workshop on Information Forensics and Security, pp. 1-6, 2016.

  • W. Wang, J. Dong and T. Tan, “Exploring DCT Coefficient Quantization Effects for Local Tampering Detection”, IEEE Transactions on Information Forensics and Security, Vol. 9, No. 10, pp. 1653-1666, 2014.

  • T. Bianchi and A. Piva, “Image Forgery Localization via Block-Grained Analysis of Jpeg Artifacts”, IEEE Transactions on Information Forensics and Security, Vol. 7, No. 3, pp. 1003-1017, 2012.

  • N.B. Abd Warif, M.Y.I. Idris, A.W.A. Wahab and R. Salleh, “An Evaluation of Error Level Analysis in Image Forensics”, Proceedings of IEEE International Conference on System Engineering and Technology, pp. 23-28, 2015.

  • I.B. Sudiatmika, and R. Fathur, “Image Forgery Detection using Error Level Analysis and Deep Learning”, Telkomnika, Vol. 17, No. 2, pp. 653-659, 2019.

  • H. Phan-Xuan, T. Le-Tien, T. Nguyen-Chinh, T. Do-Tieu, Q. Nguyen-Van and T. Nguyen-Thanh, “Preserving Spatial Information to Enhance Performance of Image Forgery Classification”, Proceedings of International Conference on Advanced Technologies for Communications, pp. 1-5, 2019.

  • Z. Ding, and F. Yun, “Robust Transfer Metric Learning for Image Classification”, IEEE Transactions on Image Processing, Vol. 26, No. 2, pp. 660-670, 2016.

  • D. Han, L. Qigang, and F. Weiguo, “A New Image Classification Method using CNN Transfer Learning and Web Data Augmentation”, Expert Systems with Applications, Vol. 95, pp. 43-56, 2018.

  • K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition”, Proceedings of IEEE International Conference on System Engineering and Technology, pp. 354-355, 2014.

  • K. He, Z. Xiangyu, R. Shaoqing and S. Jian, “Deep Residual Learning for Image Recognition”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.

  • G. Huang, L. Zhuang, V.D.M. Laurens and Q.W. Kilian “Densely Connected Convolutional Networks”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700-4708, 2017.

  • Qiang Chen and Shuicheng Yan. “Network in Network”, Proceedings of IEEE International Conference on System Engineering and Technology, pp. 1-7, 2013.

  • M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard and M. Kudlur, “Tensorflow: A System for Large-Scale Machine Learning”, Proceedings of Symposium on Operating Systems Design and Implementation, pp. 265-283, 2016.

  • F. Chollet, “Keras”, Available at https://keras.io, Accessed at 2015.


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  • Transfer Learning Approach for Splicing and Copy-Move Image Tampering Detection

Abstract Views: 199  |  PDF Views: 1

Authors

Nagaveni K. Hebbar
Department of Electronics and Telecommunication Engineering, Thadomal Shahani Engineering College, India
Ashwini S. Kunte
Department of Electronics and Telecommunication Engineering, Thadomal Shahani Engineering College, India

Abstract


Image authentication before using in any security critical applications has become necessary as the image editing tools are increasing and are handy to use in today's world. Images could be tampered in different ways, but a universal method is required to detect it. Deep learning has gained its importance because of its promising performance in many applications. In this paper a new framework for image tampering detection using Error Level Analysis (ELA) and Convolutional Neural Network (CNN) with transfer learning approach is proposed. In this method, the images are pre-processed using ELA to highlight the tampered region and are used to fine tune the entire model. Six different pre-trained models are used in the proposed framework to compare the performance in classifying the tampered and authentic images. The complexity and processing time of the proposed method is low with respect to most of the existing methods as the images are not divided into patches. The performance of the model obtained is also considerably good with an accuracy of 97.58% with Residual Network 50(ResNet50).

Keywords


Tampering Detection, Transfer Learning, Copy-Move, Splicing.

References