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Anglický jazyk
A Comparative Analysis of LBP Variants for Image Tamper Detection
Autor: Suresh Rao
This thesis explores the use of Local Binary Patterns (LBP) and Convolutional Neural Networks (CNN) for detecting image tampering, an increasingly prevalent issue in today's digital landscape. Through a comparative analysis of four LBP variants using the... Viac o knihe
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O knihe
This thesis explores the use of Local Binary Patterns (LBP) and Convolutional Neural Networks (CNN) for detecting image tampering, an increasingly prevalent issue in today's digital landscape. Through a comparative analysis of four LBP variants using the CASIA-2.0 dataset, it combines LBP's texture descriptors with CNN to enhance accuracy and robustness. The methodology involves generating local texture descriptors with LBP and feeding them into a CNN architecture trained to classify images as tampered or authentic. Despite challenges like computational complexity, the research aims to contribute to a reliable tamper detection system applicable in various real-world scenarios. Notably, Uniform LBP demonstrates superior performance in both training/testing time, achieving accuracy and F1-score exceeding 97% in image tamper detection, validating the effectiveness of the approach.
- Vydavateľstvo: LAP LAMBERT Academic Publishing
- Rok vydania: 2024
- Formát: Paperback
- Rozmer: 220 x 150 mm
- Jazyk: Anglický jazyk
- ISBN: 9786207487493
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