Image Duplication Detector

Detect image duplication and tampering in manuscript figures using computer vision algorithms

ID: 195

Description

Uses Computer Vision (CV) algorithms to scan all images in paper manuscripts to detect potential duplication or local tampering (PS traces).

Usage

bash

# Scan single PDF file
python scripts/main.py --input paper.pdf --output report.json

# Scan image folder
python scripts/main.py --input ./images/ --output report.json

# Specify similarity threshold (default 0.85)
python scripts/main.py --input paper.pdf --threshold 0.90 --output report.json

# Enable tampering detection
python scripts/main.py --input paper.pdf --detect-tampering --output report.json

# Generate visualization report
python scripts/main.py --input paper.pdf --visualize --output report.json

Parameters

Parameter	Type	Default	Required	Description
--input	string	-	Yes	Input PDF file or image folder path
--output	string	report.json	No	Output report path
--threshold	float	0.85	No	Similarity threshold (0-1), higher is stricter
--detect-tampering	flag	false	No	Enable tampering/PS trace detection
--visualize	flag	false	No	Generate visualization comparison images
--temp-dir	string	./temp	No	Temporary file directory

Output Format

json

{
  "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

text

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

python

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

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

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

bash

# 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