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Detection of Image Seam Carving Using a Novel Pattern.

Ming Lu1,2, Shaozhang Niu1

  • 1Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Computational Intelligence and Neuroscience
|December 13, 2019
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Summary

This study introduces a new method using local neighborhood magnitude occurrence patterns (LNMOP) to detect seam carving image tampering. The approach effectively identifies altered images, outperforming existing techniques.

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Area of Science:

  • Digital Image Forensics
  • Computer Vision
  • Image Processing

Background:

  • Seam carving is a content-aware image resizing technique that can be misused for image tampering.
  • Seam carving alters pixel intensity distributions, providing a potential forensic clue.
  • Existing detection methods may not fully capture the subtle changes introduced by seam carving.

Purpose of the Study:

  • To propose a novel forensic approach for detecting seam carving image forgery.
  • To introduce the Local Neighborhood Magnitude Occurrence Pattern (LNMOP) for characterizing intensity difference distributions.
  • To develop an effective feature selection method for improved detection accuracy.

Main Methods:

  • Extraction of histogram features from LNMOP and Histogram of Oriented Gradients (HOG).
  • A novel LNMOP feature selection method based on HOG feature hierarchical matching.
  • Classification using Support Vector Machine (SVM) trained on selected features.

Main Results:

  • The proposed LNMOP-based method demonstrates superior performance in detecting seam carving forgeries.
  • Feature selection based on HOG hierarchical matching effectively identifies discriminative LNMOP features.
  • Experimental results on the UCID image database confirm the approach's effectiveness.

Conclusions:

  • The LNMOP feature, combined with HOG-guided selection and SVM classification, provides a robust method for seam carving detection.
  • This approach offers a significant advancement over current state-of-the-art techniques in digital image forensics.
  • The method accurately distinguishes between original and seam-carved images, enhancing image integrity verification.