Detecting Morphed Images and Verification Itâ...

Detecting Morphed Images and Verification It’s Authenticity Using Python

Author Name : Rekshana Devi .D, Mrs. V. Latha Sivasankari

ABSTRACT With the increasing accessibility of image editing tools, the ability to detect morphed images and verify their authenticity has become crucial in domains like digital forensics, cyber security, and identity verification. This paper explores a Python-based approach to detecting morphed images and authenticating their originality. The proposed framework leverages a combination of image processing techniques, machine learning algorithms, and forensic analysis to identify subtle inconsistencies introduced during morphing. The system incorporates key features such as metadata analysis, pixel-level inconsistency detection, and deep learning-based face recognition. Forensic techniques analyze discrepancies in lighting, edges, and compression artifacts. Machine learning models are trained on authentic and manipulated datasets to classify images. Additionally, the solution integrates block chain-based digital signatures to ensure originality verification. The proposed Python implementation demonstrates a high degree of accuracy in identifying morphed images and validating authenticity. The framework is designed to be modular, scalable, and adaptable to various use cases, providing an effective tool for combating digital image manipulation in real-world applications.