Copy-Move Geometric Tampering Estimation Through Enhanced SIFT Detector Method J. S. Sujin 1,* and S. Sophia 2 1 Department of Electronics and Communication Engineering, Sri Krishna College of Technology, Coimbatore, 641042, India 2 Department of Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, India *Corresponding Author: J. S. Sujin. Email: sujinjsresearch21@outlook.com Received: 20 September 2021; Accepted: 21 October 2021 Abstract: Digital picture forgery detection has recently become a popular and sig- nificant topic in image processing. Due to advancements in image processing and the availability of sophisticated software, picture fabrication may hide evidence and hinder the detection of such criminal cases. The practice of modifying origi- nal photographic images to generate a forged image is known as digital image for- ging. A section of an image is copied and pasted into another part of the same image to hide an item or duplicate particular image elements in copy-move for- gery. In order to make the forgeries real and inconspicuous, geometric or post- processing techniques are frequently performed on tampered regions during the tampering process. In Copy-Move forgery detection, the high similarity between the tampered regions and the source regions has become crucial evidence. The most frequent way for detecting copy-move forgeries is to partition the images into overlapping square blocks and utilize Discrete cosine transform (DCT) com- ponents as block representations. Due to the high dimensionality of the feature space, Gaussian Radial basis function (RBF) kernel based Principal component analysis (PCA) is used to minimize the dimensionality of the feature vector repre- sentation, which improves feature matching efficiency. In this paper, we propose to use a novel enhanced Scale-invariant feature transform (SIFT) detector method called as RootSIFT, combined with the similarity measures to mark the tampered areas in the image. The proposed method outperforms existing state-of-the-art methods in terms of matching time complexity, detection reliability, and forgery location accuracy, according to the experimental results. The F1 score of the proposed method is 92.3% while the literature methods are around 90% on an average. Keywords: Multi sensor; data fusion; discriminator; orientation; pose; position; mean average precision; recall This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computer Systems Science & Engineering DOI: 10.32604/csse.2023.023747 Article ech T Press Science