AI Powered Fake Currency Detection System using Python Machine Learning
The rapid increase in counterfeit currency circulation poses a significant challenge to financial security and economic stability. Traditional methods of detecting fake currency often rely on manual inspection, which can be time-consuming, inaccurate, and dependent on human expertise. To address this issue, this project presents a Fake Currency Detection System that utilizes Machine Learning techniques integrated with a Django-based web application to automate the process of currency verification.
The system preprocesses uploaded currency images by converting them to grayscale, resizing them to a fixed dimension, and transforming them into numerical feature vectors. A Random Forest Classifier is trained on a dataset containing genuine and fake currency note images to classify new inputs with high efficiency. The model predicts whether a note is Genuine or Fake and provides a corresponding confidence score.
🛠️ Tech Stack Used
Frontend / Web Interface:
- Django (Python Web Framework) – Used to create the web interface for user input, displaying predictions, and managing data
- HTML5, CSS3, JavaScript – For rendering and styling web pages
- Bootstrap (optional) – For responsive UI components
- Django Templates – For dynamic web page rendering
🧠 Machine Learning / Backend Logic:
- scikit-learn – Machine Learning library used to implement algorithms like Logistic Regression, Decision Tree, Random Forest, KNN
- NumPy→ For numerical operations and matrix manipulation
- Pandas → For handling and preprocessing datasets
- joblib → To save and load the trained machine learning model
🗃️ Database:
- SQLite – Lightweight relational database used to store user data and predictions
- Django ORM (Object Relational Mapper) – Handles interaction between Django models and the SQLite database
⚙️ Tools & Environment:
- Python 3.x – Core programming language used
- PyCharm – IDE for development
- Virtualenv / pip – For managing dependencies
✅ Key Features
1. Machine Learning–Based Detection
- Uses a trained Random Forest classifier to identify fake or genuine currency.
- Image preprocessing (grayscale, resizing, flattening) ensures accurate predictions.
- Generates prediction confidence score.
- Built using Django templates with clean and responsive design.
- Simple upload form for users to submit currency note images.
- Instant display of prediction results.
3. Secure User Authentication
- User registration, login, logout, and session handling.
- Password change and profile editing features.
- Secure password hashing and validation.
4. Prediction History Tracking
- Stores all past predictions with timestamps.
- Allows deletion of individual history records.
- Includes image preview and pagination for easier navigation.
5. Integrated Preprocessing & Model Pipeline
- Images are automatically processed before prediction.
- Model loads efficiently and returns real-time results.
6. Database Management
- SQLite database for storing user details and prediction records.
- Django ORM ensures secure and efficient data handling.
7. Extendable System Architecture
- Easy to upgrade with deep learning models like CNN.
- Can support multiple currencies in future.
- Can integrate mobile camera or real-time detection features.
AI-Powered Fake Currency Detection System in Python & ML: Output Screenshot
User Sign Up
User login
Upload Note
Prediction / Detection Result Page
Prediction/Detection History
User profile
How to run the AI-Fake Currency Detection System using Python Machine Learning (ML)
1. Download the zip file of the AI-Powered Fake Currency Detection System in Python
2. Extract the file, copy fake_currency_detection the folder and paste it on the desktop
3. Open PyCharm and import the project into PyCharm
4. Install four libraries (if not installed)
pip install joblib
pip install numpy
pip install scikit-learn
pip install pandas5. Run the Project using the following command
python manage.py runserver
Now, click the URL http://127.0.0.1:800,0 and the Project will run
Login Details
*************User************
Username: john123
Password: Test@123
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