A Comparative Analysis of LBP Variants for Image Tamper Detection - Rao, Suresh;Kaur, Mandeep
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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…mehr

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Produktbeschreibung
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.
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Autorenporträt
Suresh, JRF at DRDO, working on a VR Training simulator development in Unity and C#. I have completed my masters in IT from UIET, PU-Chandigarh, India. I love Computer Science with a strong hold on DSA, C++, Python, C# and JavaScript. Exploring the unleashed power of ML/AI as an emerging domain.