Image Forgery Detection System Using Machine Learning Guidance
ABSTRACT: Digital images are widely used in social media, journalism, legal evidence, and scientific applications. The availability of advanced image editing tools has increased the risk of digital image forgery, which can lead to misinformation, privacy issues, security threats, and legal complications. Therefore, detecting image forgery accurately has become an important challenge in digital forensics. This paper presents an Image Forgery Detection System using Machine Learning techniques. The proposed system combines Error Level Analysis (ELA) with a Convolutional Neural Network (CNN) to identify manipulated images. ELA is used to highlight inconsistent compression patterns and possible tampered regions, while the CNN learns important visual features for classification. The system classifies uploaded images as either authentic or forged and provides a confidence score along with the prediction. A Python-based Flask backend is used for image processing and model integration, while HTML and CSS provide a simple and user-friendly interface. The results reported in the project demonstrate more than 90% accuracy on the test dataset. The proposed system reduces manual inspection and provides a faster approach for image authenticity verification. The system can be useful in digital forensics, media verification, security, and other applications where image authenticity is important. Future enhancements can include multi-class forgery classification, real-time detection, and cloud-based deployment.Top of Form