Fake Currency Detection Using AI and Python

Name: Fake Currency Detection Using AI and Python
Brand: UpdateGadh
SKU: UG-142
Price: 1299.00 INR
Availability: InStock

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Fake Currency Detection Using AI and Python is a Streamlit-based AI image analysis application that uses Google Gemini Generative AI to analyze Indian currency-note images. It checks visible characteristics such as the Mahatma Gandhi portrait, serial number, security thread, watermark and denomination, then provides an Original or Fake verdict with multilingual results and text-to-speech support.

Technologies: Python Machine Learning Bootstrap HTML/CSS

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Source Code

Database File

Project Report

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Setup Guide

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Project Overview

Fake Currency Detection Using AI and Python is an AI-powered image analysis application developed with Python, Streamlit and Google Gemini Generative AI. The system allows users to upload an image of an Indian currency note and analyze visible security characteristics through Gemini's image-understanding capabilities.

Unlike a traditional machine learning system that requires a separately trained currency dataset and classification model, this application sends the uploaded image to Gemini together with a specialized analysis prompt. The generated response contains a verdict, reasoning, denomination and serial number information.

Available Features

Indian currency image upload
PNG, JPG and JPEG image support
AI-based currency analysis
Original or Fake verdict
Mahatma Gandhi portrait verification
Serial number analysis
Security thread analysis
Watermark analysis
Denomination extraction
Multilingual results
English analysis
Hindi results
Marathi results
Gujarati results
Text-to-speech support
Gemini safety settings
Uploaded-image preview
Analysis result section
Loading indicator
Error and warning messages

How the System Works

Upload a clear image of an Indian currency note.
The application reads and prepares the image.
The image is combined with the specialized Gemini analysis prompt.
Gemini analyzes the visible currency characteristics.
The application receives the generated analysis.
The Original or Fake verdict is displayed.
Denomination and serial-number information are shown when identifiable.
Multilingual information is generated for supported languages.
Text-to-speech can convert the generated analysis into spoken output.

Technology Stack

Python
Streamlit
Google Gemini Generative AI
Google Generative AI API
Pyttsx3
Pathlib
Base64

Database

No database is required for the current implementation. The application directly processes the uploaded currency image and sends it to the configured Gemini AI service for analysis.

Software and Tools Required

Windows, Linux or macOS
Python 3.x
Visual Studio Code or another Python-compatible IDE
Google Gemini API key
Internet connection
Required Python packages

Important Limitation

This project is an educational AI image-analysis application and should not be treated as an official forensic currency-authentication system. A photograph may not clearly reveal security threads, watermarks, microtext, ultraviolet characteristics or other physical security features. Therefore, the generated result should be considered an image-based indication rather than a guaranteed determination of authenticity.

Future Enhancements

Dedicated machine learning classification model trained on a currency dataset
Improved image preprocessing
Automatic image-quality checking
Additional currency denominations
Detailed confidence scoring
Database-based analysis history
Improved multilingual support
Advanced accessibility features

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More Details and Full Documentation

View Fake Currency Detection Using AI and Python Details

Project Modules

1. Currency Image Upload Module
2. Image Format Validation Module
3. Image Preparation Module
4. Google Gemini AI Integration Module
5. Currency Security Analysis Module
6. Mahatma Gandhi Portrait Verification Module
7. Serial Number Analysis Module
8. Security Thread Analysis Module
9. Watermark Analysis Module
10. Denomination Extraction Module
11. Original or Fake Verdict Module
12. AI Reasoning Module
13. Multilingual Results Module
14. Hindi Translation Module
15. Marathi Translation Module
16. Gujarati Translation Module
17. Text-to-Speech Module
18. Gemini Safety Settings Module
19. Uploaded Image Preview Module
20. Analysis Results Module
21. Loading and Error Handling Module
22. Streamlit User Interface

Installation Guide

1. Install Python 3.x and Visual Studio Code.
2. Extract the project ZIP file.
3. Open the extracted project folder in Visual Studio Code.
4. Open the VS Code terminal.
5. Create a virtual environment using: python -m venv venv
6. Activate the virtual environment on Windows using: venv\Scripts\activate
7. Install the required packages using: pip install streamlit google-generativeai pyttsx3
8. Open the api_keys.py file.
9. Add your own Google Gemini API key using: api_key = &amp;amp;quot;YOUR_GEMINI_API_KEY&amp;amp;quot;
10. Save the API key configuration.
11. Start the application using: streamlit run main.py
12. Open the local Streamlit address displayed in the terminal.
13. Upload a clear PNG, JPG or JPEG image of an Indian currency note.
14. Click the Generate Analysis button.
15. Wait for Gemini to analyze the uploaded currency image.
16. Review the Original or Fake verdict and generated reasoning.
17. Check the denomination and serial number information when available.
18. Review the English, Hindi, Marathi and Gujarati results.
19. Use the text-to-speech functionality when required.
20. For deployment, store the Gemini API key using a secure secrets-management method instead of committing it directly into the project files.

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