Image Duplication Detector

Output Format

{
  "summary": {
    "total_images": 12,
    "duplicates_found": 2,
    "tampering_detected": 1,
    "processing_time": "3.5s"
  },
  "duplicates": [
    {
      "group_id": 1,
      "similarity": 0.98,
      "images": [
        {"page": 2, "index": 1, "path": "..."},
        {"page": 5, "index": 3, "path": "..."}
      ]
    }
  ],
  "tampering": [
    {
      "image": "page_3_img_2.png",
      "suspicious_regions": [
        {"x": 120, "y": 80, "width": 50, "height": 50, "confidence": 0.92}
      ]
    }
  ]
}

Requirements

opencv-python>=4.8.0
numpy>=1.24.0
Pillow>=10.0.0
PyPDF2>=3.0.0
pdf2image>=1.16.0
imagehash>=4.3.0
scikit-image>=0.21.0
matplotlib>=3.7.0

Algorithm Details

Duplication Detection

• Perceptual Hashing: Uses pHash, dHash, aHash combination to detect visually similar images
• Feature Matching: ORB feature point matching to verify similarity
• SSIM: Structural similarity index as auxiliary verification

Tampering Detection

• ELA (Error Level Analysis): Detects JPEG compression level inconsistencies
• Noise Analysis: Noise pattern anomaly detection
• Copy-Move Detection: Copy-move forgery detection
• Lighting Inconsistency: Lighting consistency analysis

Example

from scripts.main import ImageDuplicationDetector

detector = ImageDuplicationDetector(
    threshold=0.85,
    detect_tampering=True
)

results = detector.scan("paper.pdf")
detector.save_report(results, "report.json")

Notes

• Supports PDF, PNG, JPG, TIFF formats
• Large files recommended for batch processing
• Tampering detection may produce false positives, manual review recommended

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support