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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
  1. For enhanced OCR capabilities (optional):
pip install easyocr pytesseract

Usage

Running the GUI Application

  1. Recommended (Fixed Version):
python ic_detector_gui_fixed.py
  1. Final Version:
python ic_detector_gui_final.py
  1. Fallback Version:
python ic_detector_gui_fallback.py
  1. 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
Screenshot 2025-10-13 195807

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.