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CMF-iteMS: An automatic threshold selection for detection of copy-move forgery.

Nor Bakiah Abd Warif1, Mohd Yamani Idna Idris2, Ainuddin Wahid Abdul Wahab2

  • 1Department of Computer System and Technology, Faculty of Computer Science & Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia; Department of Computer System and Networking, Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang, Malaysia.

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Summary

This study introduces CMF-iteMS, an efficient CMF detection method with automatic threshold selection. It outperforms existing techniques, achieving over 90% F-score even with image resizing attacks.

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Automatic thresholdCopy-move forgeryImage forensicsThresholding

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Area of Science:

  • Digital Image Forensics
  • Computer Vision

Background:

  • Conventional CMF detection methods utilize fixed thresholds, limiting adaptability.
  • Image quality, size variations, and attacks complicate CMF detection.

Purpose of the Study:

  • To propose an efficient CMF detection method with automatic threshold selection.
  • To enhance CMF detection reliability across diverse image characteristics.

Main Methods:

  • Developed CMF-iteMS utilizing PatchMatch for CMF detection and Fourier-Mellin Transform (FMT) for feature extraction.
  • Introduced an iterative means of region size (iteMS) procedure for automatic threshold selection.
  • Evaluated CMF-iteMS against SIFT, patch matching, multi-scale, and symmetry-based methods on three datasets.

Main Results:

  • CMF-iteMS achieved over 90% F-score at the image level and 82% at the pixel level across all datasets.
  • The method demonstrated robust performance even when images were resized to a factor of 0.25.
  • Outperformed four state-of-the-art CMF detection techniques in reliability and accuracy.

Conclusions:

  • CMF-iteMS offers a flexible and efficient solution for CMF detection.
  • The automatic thresholding mechanism enhances adaptability to varying image conditions and attacks.
  • Proposed method sets a new benchmark for CMF detection accuracy and robustness.