image-duplication-detector

# 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 ```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 ``` 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