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Localize the Copy-Move Forged Region of an Image Using Improved SIFT

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Abstract

With the advancement in technology, digital images have become a very popular source of information. But this source of information is also being used to mislead people and society by altering the original image. One of the ways through which digital images are tampered with is the copy-move forgery method, wherein a part of an image is copied and pasted into another area inside the same image. To avoid and prevent any kind of anomalies, this paper focuses on ways to detect this copy and move forgery in an image using the keypoint-based approach in an efficient manner. The methodology involves detecting key points using Improved Scale Invariant Feature Transform (SIFT) based on which features are identified. Within the isolated descriptors, the deployed K-Nearest Neighbor (K-NN) classifier determines the closest match. Now in order to remove the outliers present in the features, Hierarchical Clustering is used to cluster similar descriptors. Experimental result has proved this methodology to be effective in detecting the forgery in an image for different kinds of related transformations.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India

    Rachel Selva Dhanaraj & M. Sridevi

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  1. Rachel Selva Dhanaraj
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  2. M. Sridevi
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Correspondence to Rachel Selva Dhanaraj.

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This article is part of the topical collection “Research Trends in Computational Intelligence” guest edited by Anshul Verma, Pradeepika Verma, Vivek Kumar Singh and S. Karthikeyan.

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Dhanaraj, R.S., Sridevi, M. Localize the Copy-Move Forged Region of an Image Using Improved SIFT. SN COMPUT. SCI. 5, 71 (2024). https://doi.org/10.1007/s42979-023-02388-7

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