Digital Image Forgery Detection Using SURF and ORB Technique

Authors

  • Jyoti Yadav Ramrao Adik Institute of Technology, D.Y. Patil Vidyapeeth, Nerul, Navi Mumbai
  • Nilima Dongre Ramrao Adik Institute of Technology, D.Y. Patil Vidyapeeth, Nerul, Navi Mumbai

Keywords:

Copy move forgery, Block based, SURF algorithm, ORB algorithm, SVM and EM algorithm

Abstract

Copy-move forgery involves duplicating part of an original image and pasting it elsewhere within the same image to disguise manipulations. Detecting such forgeries is crucial for verifying image authenticity. This research explores keypoint-based approaches for copy-move detection, specifically SURF and ORB. SURF (Speeded Up Robust Features) identifies interest points using the Hessian matrix and describes them with Haar wavelet responses. ORB (Oriented FAST and Rotated BRIEF) uses FAST keypoint detection and binary BRIEF description for efficiency. After extracting SURF and ORB features, SVM and EM classifiers categorize images as forged or genuine. Performance is evaluated using accuracy, precision, recall and F1 score. Results demonstrate ORB+SVM and ORB+EM outperform SURF+EM on all metrics. This highlights ORB's advantages over SURF for copy-move detection when paired with SVM or EM. ORB provides faster feature extraction and description leading to better classification. In conclusion, keypoint methods like ORB show promise for copy-move forgery detection. ORB's efficiency and discriminative power, combined with SVM or EM classification, can effectively identify image manipulations. This research provides valuable insights into optimal feature extraction and machine learning techniques for enhanced forgery detection.

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Published

12.01.2024

How to Cite

Yadav, J. ., & Dongre, N. . (2024). Digital Image Forgery Detection Using SURF and ORB Technique. International Journal of Intelligent Systems and Applications in Engineering, 12(12s), 696 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4553

Issue

Vol. 12 No. 12s (2024)

Section

Research Article

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