Abstract
Copy-move image forgery detection (CMIFD) via SIFT algorithm is one of the emerging and effective key-point based strategies. This algorithm is robust against large-scale geometric transformations and various attacks during the forgery process. CMIFD via SIFT algorithm accurately localizes the tampered regions rich in structural content at a cost of key-point matching problem and provides inferior detection accuracy with higher rate of false alarm in localizing relatively smoothed and little structured forgery regions. Natural images are highly structured. Pixels in these images preserve sufficient spatial and structural correlation among each other which should be preserved during the feature matching process. However, SIFT algorithm has no provision to preserve structural correlation among key-points. Consequently, detects insufficient number of key-points for images rich in structural content. To alleviate these bottlenecks, we have proposed an efficient CMIFD scheme by (i) integrating spatial and structural information additionally in the SIFT feature descriptor and (ii) utilized an adaptive strategy in selecting optimal number of matched key-points from a pool of candidate key-points. All these modifications enable to minimize the key-point matching problem and localize both structured and smoothed tampered regions accurately. Outperforming behavior of the proposed method is validated via several experimental result analyses.
Supported by NIT Meghalaya.
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Hansda, R., Nayak, R., Balabantaray, B.K. (2021). Copy-Move Image Forgery Detection Using Spatio-Structured SIFT Algorithm. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1376. Springer, Singapore. https://doi.org/10.1007/978-981-16-1086-8_3
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