Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 9 , Issue 1 , PP: 72-78, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
L. Chitirap Paavai 1 * , V. Vadivu 2 , L. Krishnan 3 *
Doi: https://doi.org/10.54216/JAIM.090107
Creating images is one of the focuses of digital image processing. There are multiple techniques to spot image fraud. This work proposes a new approach to detect attacks that mimic Copy-Move forgeries. The proposed method applies DWT on the input image to create a reduced dimensional representation of the image. After that, the compressed image is divided into overlapping blocks. After these blocks are sorted, phase correlation is utilized as a similarity criterion to find duplicate blocks. Due to DWT usage, the lowest-level picture representation is first employed for detection. This work also covers the examination of numerous limits that are imposed to the input image, and the results are used in the analysis that follows.
Copy-Move forgery , Digital forensics , DWT , Phase correlation
[1] S. Khan and A. Kulkarni, “Reduced Time Complexity for Detection of Copy-Move Forgery using Discrete Wavelet Transform,” International Journal of Computer Applications, vol. 6, no. 7, pp. 1-5, Sept. 2010.
[2] Z. Ting and W. Rang-ding, “Copy Move Forgery Detection Based on SVD in Digital Image,” IEEE, 2009.
[3] G. Li, Q. Wu, D. Tu, and S. Sun, “A Sorted Neighbourhood Approach for Detecting Duplicated Regions in Image Forgeries Based on DWT and SVD,” in IEEE International Conference on Multimedia & Expo, 2007.
[4] J. Fridrich, D. Soukal, and J. Lukas, “Detection of Copy-Move Forgery in Digital Images,” in Proceedings of the Digital Forensic Research Workshop, Cleveland, OH, USA, 2003.
[5] A. C. Popescu and H. Farid, “Exposing Digital Forgeries by Detecting Duplicated Image Regions,” Dartmouth College, Hanover, NH, USA, TR2004-515, 2004.
[6] G. Li, Q. Wu, D. Tu, and S. Sun, “A Sorted Neighborhood Approach for Detecting Duplicated Regions in Image Forgeries Based on DWT and SVD,” in IEEE International Conference on Multimedia & Expo, 2007.
[7] D. A. Talbert and D. Fisher, “An Empirical Analysis of Techniques for Constructing and Searching k-Dimensional Trees,” in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 26-33, 2000.
[8] I.-K. Jung and S. Lacroix, “A Robust Interest Point Matching Algorithm,” in International Conference on Computer Vision, 2001.
[9] N. Sugirtham, R. S. Jenny, B. Thiyaneswaran, and S. Kumarganesh, “Modified Playfair for Text File Encryption and Meticulous Decryption with Arbitrary Fillers by Septenary Quadrate Pattern,” International Journal of Networked and Distributed Computing, vol. 12, pp. 108–118, 2024.
[10] S. Kumarganesh, S. Anthoniraj, T. Senthil Kumar, P. Elayaraja, et al., “A Novel Approach for Image Forgery Detection Using Deep Learning Techniques,” Journal of Image Processing, vol. 15, no. 3, pp. 250-262, 2022.
[11] T. Thangaraj, B. Thiyaneswaran, and S. Kumarganesh, “Early Detection of Melanoma Images Using Gray Level Co-occurrence Matrix Features and Machine Learning Techniques for Effective Clinical Diagnosis,” International Journal of Imaging Systems and Technology, vol. 31, no. 2, pp. 682-694, 2021.
