Image Forgery Detection System

With the rapid expansion of digital media and online information sharing, ensuring the authenticity of digital images has become increasingly critical in journalism, legal investigations, cybersecurity, and social networking platforms. The availability of advanced image editing tools has made digital image manipulation easier and more sophisticated. This paper presents an Image Manipulation Detection System developed using Python that identifies forged or tampered images through digital forensic analysis techniques. The proposed system detects multiple types of image forgery, including copy–move manipulation, double JPEG compression artifacts, and Colour Filter Array (CFA) inconsistencies. Additionally, metadata analysis is performed to identify suspicious modifications in image properties. The system integrates these forensic techniques within a graphical user interface (GUI) to provide an efficient and user-friendly detection framework. Experimental results demonstrate the system’s ability to accurately detect common image tampering methods while maintaining reliability and efficiency. The proposed framework serves as a practical tool for digital image authentication and contributes to combating misinformation and digital fraud.