Image Duplication Detector

---

name: image-duplication-detector

description: Detect image duplication and tampering in manuscript figures using computer

vision algorithms

version: 1.0.0

category: Integrity

tags: []

author: AIPOCH

license: MIT

status: Draft

risk_level: Medium

skill_type: Tool/Script

owner: AIPOCH

reviewer: ''

last_updated: '2026-02-06'

---

# Image Duplication Detector

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

# 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

{
  "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

Tampering Detection

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

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

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

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

- Performance optimization

- Additional feature support