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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Region duplication forgery detection technique based on SURF and HAC.

Parul Mishra¹, Nishchol Mishra, Sanjeev Sharma

¹School of Information Technology, RGPV, Bhopal, Madhya Pradesh 462036, India.

Thescientificworldjournal

|December 7, 2013

Summary

This study introduces a novel method for detecting copy-move image forgery using Speeded Up Robust Features (SURF) and Hierarchical Agglomerative Clustering (HAC). The approach effectively identifies duplicated image regions, even after various image transformations.

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Detection of Copy Number Alterations Using Single Cell Sequencing

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Area of Science:

Digital Image Forensics
Computer Vision

Background:

Copy-move forgery is a prevalent digital image manipulation technique.
Sophisticated image editing tools make visual detection of forgeries challenging.
Tampered regions are often robust to various transformations like scaling, rotation, and compression.

Purpose of the Study:

To develop an efficient and robust method for detecting region duplication forgery.
To address the challenge of identifying copied and pasted image segments, even after transformations.

Main Methods:

Utilizes Speeded Up Robust Features (SURF) for keypoint and feature detection.
Employs Hierarchical Agglomerative Clustering (HAC) to group matched keypoints.
Identifies copied and pasted regions through feature matching and clustering.

Main Results:

The SURF and HAC-based method successfully detects duplicated regions in images.
The approach demonstrates robustness against common image transformations applied to forged areas.
Accurate identification of copied and pasted segments is achieved.

Conclusions:

The proposed SURF and HAC method offers an effective solution for copy-move forgery detection.
This technique provides a reliable means to identify tampered image regions in digital forensics.
The method is suitable for detecting robust forgeries commonly encountered in practice.