Texture-Based Image Forgery Detection Using Wavelet Transform and Adaptive SVM Optimization

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Dr.Rohini Palve; Dr.shudhodhan Bokefode; Dr.varsha Bodade; Dr.Ramesh Shabade; Dr.Kishor Sakure

Texture-Based Image Forgery Detection Using Wavelet Transform and Adaptive SVM Optimization

Authors: Dr.Rohini Palve, Dr.shudhodhan Bokefode, Dr.varsha Bodade, Dr.Ramesh Shabade, Dr.Kishor Sakure

Unique Paper ID: 183388
Volume: 12
Issue: 3
PageNo: 1237-1242

Keywords: Image Forgery Discrete Wavelet Transform Support Vector Machine Bee Scout Optimization Texture Analysis Digital Forensics

Abstract:
In the digital era, the integrity of visual content is often compromised due to the increasing accessibility of advanced image editing tools, making image forgery a critical issue in fields such as forensics, journalism, and legal investigations. This paper presents a texture-based image forgery detection framework that leverages Discrete Wavelet Transform (DWT) for robust texture feature extraction and employs an adaptive Support Vector Machine (SVM) optimized through a Bee Scout-inspired met heuristic algorithm. The DWT captures multi-scale texture variations caused by tampering, while the adaptive SVM model enhances classification performance by dynamically tuning hyper parameters based on selected features. Experimental evaluations on a benchmark dataset demonstrate that the proposed method achieves superior accuracy in detecting tampered regions, reflected by higher Peak Signal-to-Noise Ratio (PSNR) and lower False Rejection Rate (FRR) compared to conventional DWT-SVM approaches. This approach provides a scalable and efficient solution for authenticating digital images in real-world forensic applications.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: 3
PageNo: 1237-1242

Texture-Based Image Forgery Detection Using Wavelet Transform and Adaptive SVM Optimization

Available:https://ijirt.org/article?manuscript=183388

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