Feature Extraction and Analysis of SIFT features for ELA of Authentic and Forged Images


  • Rupali M. Bora Research Scholar, Department of Computer Engineering, MET Institute of Engineering, Nashik affiliated to Savitribai Phule Pune University, India
  • Mahesh R. Sanghavi Professor, Department of Computer Engineering, SNJB’s L.S.K.B.J. College of Engineering, Chandwad, Nashik, India


Copy-move, Error Level Analysis (ELA), Image forgery, Scale Invariant Feature Transform (SIFT), Spliced images


With the advancements of technology in current era, everyone faces a challenge to identify digitally manipulated images. It is not easy to discriminate the original and forged images. For digital image tampering, image splicing and copy-move forgeries are very much well-known and common techniques. Image forgery is detected and spotted based on feature descriptor of an image. It is a concise and important local descriptor which is to be applied to grasp hierarchical representations from the input images. The significant correlation among nearby pixels has been identified by deep learning-based methods. It prefers locally grouped networks rather than one-to-one networks among all pixels. In the conducted research, the primary objective was to discern the authenticity of images using an integrated approach involving Error Level Analysis (ELA), Scale-Invariant Feature Transform (SIFT) features, and specific considerations for image types. These are Authentic, Copy-Move, and Spliced. The images under investigation were exclusively of JPEG format with size of 384x256 pixels. The Average SIFT feature values for the Authentic images consistently surpassed those of both Copy-Move and Spliced images. This discrepancy in feature values across the three categories, considering the standardized image format, presented a distinct opportunity for classification.


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How to Cite

Bora, R. M. ., & Sanghavi, M. R. . (2024). Feature Extraction and Analysis of SIFT features for ELA of Authentic and Forged Images . International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 368–373. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4848



Research Article