Determination of the Optimal Threshold Value and Number of Keypoints in Scale Invariant Feature Transform-based Copy-Move Forgery Detection

Authors

  • R Rizal Isnanto Diponegoro University http://orcid.org/0000-0002-6044-0644
  • Ajub Ajulian Zahra Diponegoro University
  • Imam Santoso Diponegoro University
  • Muhammad Salman Lubis Diponegoro University

Abstract

The copy-move forgery detection (CMFD) begins with the preprocessing until the image is ready to process. Then, the image features are extracted using a feature-transform-based extraction called the scale-invariant feature transform (SIFT). The last step is features matching using Generalized 2 Nearest-Neighbor (G2NN) method with threshold values variation. The problem is what is the optimal threshold value and number of keypoints so that copy-move detection has the highest accuracy. The optimal threshold value and number of keypoints had determined so that the detection has the highest accuracy. The research was carried out on images without noise and with Gaussian noise.

Author Biographies

R Rizal Isnanto, Diponegoro University

Lecturer in Computer Engineering Department, in Master of Electrical Engineering Program, and in Master of Information System Program, Diponegoro University

Ajub Ajulian Zahra, Diponegoro University

Lecturer in Electrical Engineering Department, Diponegoro University

Imam Santoso, Diponegoro University

Lecturer in Electrical Engineering Department, Diponegoro University

Muhammad Salman Lubis, Diponegoro University

Student in Electrical Engineering Department, Diponegoro University

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This cover image depicts an example of the effect of the copy-move forgery operation on an image: (a) original image with two traffic signs and (b) forged image without traffic signs

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Published

2024-04-19

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Section

Image Processing