Image Forgery Detection is an AI-driven system designed to identify manipulated, forged, and AI-generated images using advanced Deep Learning and Computer Vision techniques. With the rapid growth of image editing software and generative AI models, distinguishing authentic images from fabricated content has become increasingly challenging.
This project leverages Machine Learning, Deep Learning, and Generative AI methodologies to analyze visual patterns, inconsistencies, metadata, and hidden artifacts within digital images. The system is capable of detecting image tampering, synthetic image generation, and other forms of digital manipulation that may not be visible to the human eye.
The primary objective of this project is to enhance digital trust by providing an automated solution for image authenticity verification. Such a system can support applications in digital forensics, cybersecurity, journalism, social media monitoring, legal investigations, and misinformation detection.
- AI-Based Image Forgery Detection
- Deep Learning-Powered Image Classification
- Detection of Manipulated and AI-Generated Images
- Computer Vision-Based Feature Extraction
- Automated Authenticity Verification
- User-Friendly Web Interface
- Secure User Authentication
- Real-Time Image Analysis and Prediction
- Python
- TensorFlow / Keras
- OpenCV
- NumPy & Pandas
- Deep Learning
- Generative AI
- Computer Vision
- Flask / Streamlit
To develop an intelligent and reliable image authentication system that detects forged and AI-generated images with high accuracy, helping organizations and individuals verify the authenticity of digital content and reduce the spread of misinformation.
The landing page of the application where users can access all major functionalities.
Provides information about the project, objectives, and technologies used.
Users can submit their feedback and suggestions regarding the application.
Registered users can securely log in to access the platform.
New users can create an account and start using the system.
Users can upload a single image to perform image verification and analysis.
Users can upload two images and compare them for similarity detection.
Displays similarity score and analysis results between the uploaded images.
Shows the final prediction and detailed output generated by the system.
- Python
- Flask
- HTML5
- CSS3
- JavaScript
- Bootstrap
- Machine Learning
- OpenCV
- NumPy
- Pandas
- Deep Learning-Based Image Verification
- Real-Time Image Processing
- Cloud Deployment
- User Activity Dashboard
- Enhanced AI Models
Nitish Singh