Abstract
In view of the problem existing in abusive using of image copy-move forgeries, this paper proposes an image forensics algorithm for detecting copy-move forgery based on improved PCA-SIFT. The present method works first by extracting features of an image and then reducing its dimensionality, and the method uses k-nearest neighbor to operate forgery detection. Owing to the similarity between pasted region and copied region, the descriptors are then matched between each other to seek for any possible forgery in images. Extensive experimental results are presented to confirm that the algorithm is able to precisely individuate the tampered image and quantify its robustness and sensitivity to image post-processing and offer a considerable improvement in time efficiency.
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References
Gavin, L., Shih, F. Y., & Liao, H.-Y. M. (2013). An efficient expanding block algorithm for image copy-move forgery detection. Information Sciences, 239, 253–265.
Christlein, V., Riess, C., Jordan, J., Riess, C., & Angelopoulou, E. (2012). An evaluation of popular copy-move forgery detection approaches. Information Forensics and Security, 7(6), 1841–1854.
Jessica, Fridrich., David, Soukal., Jan, Lukas. (2003). Detection of copy–move forgery in digital images. Proceedings of Digital Forensic Research Workshop (DFRWS’03) (pp. 55–61). Cleveland, OH: IEEE Computer Society.
Babak, M., & Stanislav, S. (2007). Detection of copy–move forgery using a method based on blur moment invariants. Forensic Science International, 171(2–3), 180–189.
Mohammad Akbarpour, S., Mohd. Aizaini, M., Mohd. Foad, R., & Babak, M. (2013). Efficient image duplicated region detection model using sequential block clustering. Digital Investigation, 10(1), 73–84.
Hailing, Huang., Weiqiang, Guo., Yu, Zhang. (2008). Detection of copy-move forgery in digital images using SIFT algorithm. Proceedings of IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application (PACIIA’08) (vol. 2, pp. 272–276). Wuhan: IEEE Computer Society.
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
Yan, Ke., Rahul, Sukthankar. (2004). PCA-SIFT: A more distinctive representation for local image descriptors. Proceedings of 2004 I.E. Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’04) (vol. 2, pp. 506–513). Washington, DC: IEEE Computer Society.
Acknowledgements
Project supported by the National Science and Technology Support Plan Project (No. 2013BAK07B04) and Natural Science Foundation of Hebei Province. China (No. F2013201170).
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Li, K., Li, H., Yang, B., Meng, Q., Luo, S. (2014). Detection of Image Forgery Based on Improved PCA-SIFT. In: Wong, W.E., Zhu, T. (eds) Computer Engineering and Networking. Lecture Notes in Electrical Engineering, vol 277. Springer, Cham. https://doi.org/10.1007/978-3-319-01766-2_78
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DOI: https://doi.org/10.1007/978-3-319-01766-2_78
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