An Enhanced Hybrid Method based on Local and Frequency Feature Extraction for Image Copy Move Forgery Detection

  • Shirin Nayerdinzadeh Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
  • Mohammad Reza Yousefi Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
Keywords: Copy Move Forgery Detection, Scale Invariant Feature Transform, Discrete Wavelet Transform, , Local Binary Pattern, Symmetrical Images


Today, due to the advent of the powerful photo editing software packages, it has become relatively easy to create forgery images. Recognizing the correctness of digital images becomes important when those images are used as evidence in legal, forensic, industrial, and military applications. One of the most common ways to forge images is copy move forgery, in which one part of the image is copied and pasted in another part of the same image. So far, various methods have been proposed for detecting copy move forgery, but these methods are not able to detect copy move forgery with different challenges of noise, rotation, scale, and detection of symmetrical images with high accuracy. In this paper, an enhanced hybrid method based on local and frequency feature extraction is presented for image copy move forgery detection, which has a very high resistance to above challenges, both individually and simultaneously and has provided good identification accuracy. In this method, the combination of Discrete Wavelet Transform, Scale Invariant Feature Transform and Local Binary Pattern are used simultaneously. The forged area is chosen in such a way that at least both methods used in this proposed method have consensus about the forgery of that area. Various experiments and analyses on the MICC database show that the proposed methods, despite the above challenges, have reached the accuracy of 98.81% both separately and simultaneously, which shows significant improvement compared to other methods used in this field.


[1] Y. Huang, W. Lu, W. Sun, and D. Long, "Improved DCT-based detection of copy-move forgery in images," Forensic Science International, vol. 206, pp. 178-184, 2011.
[2] R. Davarzani, K. Yaghmaie, S. Mozaffari, and M. Tapak, "Copy-move forgery detection using multiresolution local binary patterns," Forensic Science International, vol. 231, pp. 61-72, 2013.
[3] G. Lynch, F. Y. Shih, and H.-Y. M. Liao, "An efficient expanding block algorithm for image copy-move forgery detection," Information Sciences, vol. 239, pp. 253-265, 2013.
[4] J.-C. Lee, C.-P. Chang, and W.-K. Chen, "Detection of copy–move image forgery using histogram of orientated gradients," Information Sciences, vol. 321, pp. 250-262, 2015.
[5] E. Silva, T. Carvalho, A. Ferreira, and A. Rocha, "Going deeper into copy-move forgery detection: Exploring image telltales via multi-scale analysis and voting processes," Journal of Visual Communication and Image Representation, vol. 29, pp. 16-32, 2015.
[6] A. V. Malviya and S. A. Ladhake, "Pixel Based Image Forensic Technique for Copy-move Forgery Detection Using Auto Color Correlogram," Procedia Computer Science, vol. 79, pp. 383-390, 2016.
[7] X. Bi, C.-M. Pun, and X.-C. Yuan, "Multi-Level Dense Descriptor and Hierarchical Feature Matching for Copy–Move Forgery Detection," Information Sciences, vol. 345, pp. 226-242, 2016.
[8] F. Yang, J. Li, W. Lu, and J. Weng, "Copy-move forgery detection based on hybrid features," Engineering Applications of Artificial Intelligence, vol. 59, pp. 73-83, 2017.
[9] H. A. Alberry, A. A. Hegazy, and G. I. Salama, "A fast SIFT based method for copy move forgery detection," Future Computing and Informatics Journal, vol. 3, pp. 159-165, 2018.
[10] C.-M. Pun and J.-L. Chung, "A two-stage localization for copy-move forgery detection," Information Sciences, vol. 463-464, pp. 33-55, 2018.
[11] X. Bi and C.-M. Pun, "Fast copy-move forgery detection using local bidirectional coherency error refinement," Pattern Recognition, vol. 81, pp. 161-175, 2018.
[12] T. Mahmood, Z. Mehmood, M. Shah, and T. Saba, "A robust technique for copy-move forgery detection and localization in digital images via stationary wavelet and discrete cosine transform," Journal of Visual Communication and Image Representation, vol. 53, pp. 202-214, 2018.
[13] B. Soni, P. K. Das, and D. M. Thounaojam, "Geometric transformation invariant block based copy-move forgery detection using fast and efficient hybrid local features," Journal of Information Security and Applications, vol. 45, pp. 44-51, 2019.
[14] A. Hegazi, A. Taha, and M. M. Selim, "An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal," Journal of King Saud University - Computer and Information Sciences, 2019.
[15] J.-L. Zhong and C.-M. Pun, "Two-pass hashing feature representation and searching method for copy-move forgery detection," Information Sciences, vol. 512, pp. 675-692, 2020.
[16] K. B. Meena and V. Tyagi, "A copy-move image forgery detection technique based on tetrolet transform," Journal of Information Security and Applications, vol. 52, p. 102481, 2020.
[17] X. Chao-jian and G. San-xue, "Image Target Identification of UAV Based on SIFT," Procedia Engineering, vol. 15, pp. 3205-3209, 2011.
[18] A. Batur, G. Tursun, M. Mamut, N. Yadikar, and K. Ubul, "Uyghur Printed Document Image Retrieval Based on SIFT Features," Procedia Computer Science, vol. 107, pp. 737-742, 2017.
[19] Y. Ji, L. Sun, Y. Li, and D. Ye, "Detection of bruised potatoes using hyperspectral imaging technique based on discrete wavelet transform," Infrared Physics & Technology, vol. 103, p. 103054, 2019.
[20] C.-H. Hsia and J.-M. Guo, "Efficient modified directional lifting-based discrete wavelet transform for moving object detection," Signal Processing, vol. 96, pp. 138-152, 2014.
[21] K. Gopala Krishnan and P. T. Vanathi, "An efficient texture classification algorithm using integrated Discrete Wavelet Transform and local binary pattern features," Cognitive Systems Research, vol. 52, pp. 267-274, 2018.
How to Cite
Nayerdinzadeh, S., & Yousefi, M. (2021). An Enhanced Hybrid Method based on Local and Frequency Feature Extraction for Image Copy Move Forgery Detection. Majlesi Journal of Electrical Engineering, 15(3), 69-80.