IC Detection System

A comprehensive system for detecting fake vs real integrated circuits (ICs) using image analysis and text extraction.

Features

• GUI Interface: User-friendly Tkinter interface for easy image upload and analysis
• Text Extraction: Extracts text from IC images using advanced image processing
• Database Verification: Cross-references extracted text against IC_DATA.csv database Where Data is extracted from OME Datasheet
• Fake Detection: Advanced algorithms to detect fake ICs based on multiple quality indicators
• Responsive Design: Clean, responsive GUI that adapts to different screen sizes

Files

• ic_detector_gui_fixed.py - Main GUI application (recommended)
• ic_detector_gui_final.py - GUI with multiple OCR methods
• ic_detector_gui_fallback.py - GUI with basic analysis (fallback)
• ic_detector_gui.py - GUI with PaddleOCR integration (requires PaddleOCR)
• IC_DATA.csv - Database of 60+ IC types from various manufacturers
• Real_IC/ - Folder containing real IC images for testing
• Fake_IC/ - Folder containing fake IC images for testing
• IC.ipynb - Jupyter notebook with original analysis code

Installation

1. Install required Python packages:

pip install opencv-python numpy pandas pillow tkinter

2. For enhanced OCR capabilities (optional):

pip install easyocr pytesseract

Usage

Running the GUI Application

1. Recommended (Fixed Version):

python ic_detector_gui_fixed.py

2. Final Version:

python ic_detector_gui_final.py

3. Fallback Version:

python ic_detector_gui_fallback.py

4. With PaddleOCR (if installed):

python ic_detector_gui.py

Using the Interface

1. Select Image: Click "Select Image" to choose an IC image file
2. Analyze: Click "Analyze" to process the image
3. View Results: See the analysis results in the right panel

Analysis Results

The system provides:

• Prediction: REAL or FAKE with confidence percentage
• Extracted Text: Text found on the IC
• Database Match: Information about matched IC from database
• Quality Metrics: Image sharpness, contrast, brightness, edge density
• Fake Indicators: Specific reasons why an IC might be fake

Database

The IC_DATA.csv file contains information about 60+ IC types from manufacturers including:

• Texas Instruments
• Analog Devices
• Maxim Integrated
• Linear Technology
• National Semiconductor
• STMicroelectronics
• Microchip
• Intel
• Fairchild Semiconductor
• ON Semiconductor

Detection Logic

The system uses multiple criteria to determine if an IC is fake:

1. Image Quality: Sharpness, contrast, brightness, edge density
2. Text Extraction: Number and quality of text elements
3. Database Matching: Comparison with known IC patterns
4. Content Analysis: Detection of unusual characters or patterns
5. Consistency Checks: Verification of text consistency

Supported Image Formats

• JPEG (.jpg, .jpeg)
• PNG (.png)
• BMP (.bmp)
• TIFF (.tiff)

System Requirements

• Python 3.7+
• Windows/Linux/macOS
• Minimum 4GB RAM
• OpenCV, NumPy, Pandas, Pillow, Tkinter

Troubleshooting

Common Issues

1. "No module named 'paddleocr'": Use the fallback version instead
2. Poor text extraction: Ensure images are well-lit and high resolution
3. GUI not responding: Check if image file is corrupted or too large

Performance Tips

• Use high-resolution images (800x600 or higher)
• Ensure good lighting and contrast
• Avoid blurry or distorted images
• Use images with clear text markings

Contributing

To add new IC types to the database:

1. Edit IC_DATA.csv
2. Add manufacturer, part number, and text patterns
3. Test with sample images

License

This project is for educational and research purposes.