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Local Features Matching Assistance-Based Image Forgery Detection

Layman’s Abstract
This paper introduces two techniques to detect similar areas in a tampered (forged) image.

First Method: The image is broken into smaller parts, and special features (SIFT keys) are extracted from each part. A similarity score helps find matching areas within the image.
Second Method: It focuses on shapes and borders of objects in the image, using a genetic algorithm (GA)—a smart search technique inspired by natural evolution (inheritance, mutation, selection, and crossover). GA helps compare different image sections efficiently.
The study finds that both methods work well in detecting similar areas in an image, with genetic algorithms proving highly effective in different search conditions. This research can help improve forgery detection in images.

View book:- https://doi.org/10.9734/bpi/srnta/v9/...

#forgery #ForgeryDetection #image #imageforgery


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