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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Robust image hashing using ring partition-PGNMF and local features.

Ram Kumar Karsh1, R H Laskar1, Bhanu Bhai Richhariya2

  • 1Department of Electronics and Communication Engineering, NIT Silchar, Silchar, Assam 788010 India.

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|December 10, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a robust image hashing technique combining global and local features for secure image authentication. The method effectively detects forged areas while remaining invariant to content-preserving modifications.

Keywords:
Image hashingMultimedia securityPGNMFRing partitionSaliency detection

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Area of Science:

  • Multimedia security
  • Digital image forensics
  • Computer vision

Background:

  • Image authentication is crucial in multimedia technology due to sophisticated image editing tools.
  • Existing image hashing methods struggle to balance rotation invariance, discrimination, sensitivity, and localization with minimal hash length.

Purpose of the Study:

  • To propose a perceptually robust image hashing technique.
  • To achieve rotation invariance, high discrimination, and forgery localization.
  • To minimize hash length for efficient image authentication.

Main Methods:

  • A novel image hashing technique combining global and local features.
  • Global features extracted using ring partitioning for rotation invariance and Projected Gradient Non-negative Matrix Factorization (PGNMF) for dimension reduction.
  • Local features extracted from salient regions for forgery localization.

Main Results:

  • The proposed method demonstrates superior discrimination capability compared to global-feature-only methods.
  • Experimental results on large image datasets confirm robustness against content-preserving operations.
  • The system accurately localizes counterfeit areas in manipulated images.

Conclusions:

  • Combining global and local features offers robustness against content-preserving operations and strong discriminative power.
  • The proposed image hashing system is suitable for applications in image authentication, forensic evidence, and image retrieval.