Abstract
This paper suggests a hybrid Copy-Move image forgery detection method using phase adaptive Spatio-structured SIFT algorithm which enables to localize forgery regions (FRs) in the presence of combination of intermediate and post-processing attacks. Moreover, the proposed method well localizes varied types and mixed-sized FRs at greater accuracy. Key contributions of the proposed method are: (i) detection of matched key-points via Spatio-structured SIFT (S-SIFT) algorithm; (ii) formation of larger blocks around the matched key-points followed by the division of larger blocks into several non-overlapping blocks; (iii) representation of non-overlapping blocks via feature descriptors based on the histogram of oriented phase congruency (HoPC); (iv) block matching via 2NN matching strategy to localize the final forged areas. S-SIFT algorithm integrates spatial and structural information additionally in the SIFT feature descriptor for detecting sufficient numbers of key-points at relatively smoothed and little structured FRs. Formation of larger blocks around the matched key-points enables to enhance the probability of detecting any sized FRs, whereas non-overlapping division of blocks generates a lesser number of blocks to be matched. Hence, the computational time of the matching process will be reduced. Feature descriptors based on HoPC are robust to noise, invariant to various geometric transforms, insensitive to change in image contrast and non-uniform illumination. Performance assessment of the proposed work in the presence of different attacks is validated both at image-level and pixel-level on Benchmark datasets, such as MICC-F220, GRIP, and CoMoFoD, and found to outperform the state-of-the-art methods.
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This article is part of the topical collection “Progresses in Image Processing” guest edited by P. Nagabhushan, Peter Peer, Partha Pratim Roy and Satish Kumar Singh.
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Hansda, R., Nayak, R., Balabantaray, B.K. et al. Copy-Move Image Forgery Detection Using Phase Adaptive Spatio-structured SIFT Algorithm. SN COMPUT. SCI. 3, 46 (2022). https://doi.org/10.1007/s42979-021-00903-2
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DOI: https://doi.org/10.1007/s42979-021-00903-2