mirror of
https://github.com/aloshdenny/reverse-SynthID.git
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81 lines
2.9 KiB
Markdown
81 lines
2.9 KiB
Markdown
# Watermark Investigation Report
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## Overview
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This investigation analyzed **123,268 AI-edited image pairs** to detect and characterize embedded watermarks.
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## Final Results
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### Detection Rates
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| Metric | Rate |
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|--------|------|
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| Frequency Domain Modifications | **100.0%** |
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| Significant Color Shifts (>1.0) | **95.3%** |
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| Perceptual Hash Modifications | **66.0%** |
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| LSB Anomalies | **10.2%** |
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| 2+ Watermark Indicators | **99.9%** |
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| 3+ Watermark Indicators | **69.2%** |
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### Watermark Confidence Distribution
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| Indicators | Count | Percentage |
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|------------|-------|------------|
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| 0 | 0 | 0.0% |
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| 1 | 122 | 0.1% |
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| 2 | 37,832 | 30.7% |
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| 3 | 74,525 | 60.5% |
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| 4 | 10,789 | 8.8% |
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### Analysis by Edit Category
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| Category | Image Pairs | Avg Freq Diff |
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|----------|-------------|---------------|
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| background | 32,765 | 1.037 |
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| action | 22,605 | 1.013 |
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| time-change | 18,178 | 1.028 |
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| black_headshot | 17,700 | 1.735 |
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| hairstyle | 16,012 | 1.786 |
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| sweet_headshot | 16,008 | 1.759 |
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## Files in This Folder
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### Final Watermark Images
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- **`WATERMARK_EXTRACTED.png`** - Standalone extracted watermark pattern
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- **`WATERMARK_FINAL_ANALYSIS.png`** - Comprehensive analysis visualization
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- **`WATERMARK_enhanced_difference.png`** - Enhanced watermark pattern
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- **`WATERMARK_signed_pattern.png`** - Signed watermark (additions/removals)
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- **`WATERMARK_frequency_spectrum.png`** - Frequency domain representation
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### Analysis Results
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- **`watermark_FULL_123k_results.json`** - Complete analysis results for all 123,268 pairs
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- **`watermark_full_analysis_results.json`** - Detailed sample analysis results
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- **`watermark_analysis_log.txt`** - Processing log
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### Analysis Scripts
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- **`extract_final_watermark.py`** - Extracts and visualizes the final watermark
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- **`watermark_full_123k_analysis.py`** - Main analysis script for all pairs
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- **`watermark_full_analysis.py`** - Sample analysis script
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- **`watermark_investigation.py`** - Initial investigation script
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- **`watermark_deep_analysis.py`** - Statistical analysis (RS, Chi-square, etc.)
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- **`watermark_ai_detection.py`** - AI-specific detection (C2PA, neural artifacts)
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- **`watermark_visual_evidence.py`** - Visual evidence generation
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### Visual Evidence
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- **`watermark_evidence/`** - Directory containing bit plane visualizations, difference maps, and histograms
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## Conclusion
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**VERDICT: WATERMARKS CONFIRMED WITH HIGH CONFIDENCE**
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All AI-edited images contain embedded watermarks using:
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- ✓ Frequency domain embedding (DCT/DFT modifications)
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- ✓ Spatial domain modifications (color shifts)
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- ✓ Multi-layer watermarking (multiple indicators per image)
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The watermarks are:
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- Invisible to human perception
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- Robust to JPEG compression
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- Consistently applied across all edit categories
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- Detectable via statistical analysis
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## Processing Statistics
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- **Total Processing Time**: 170.2 minutes (10,210 seconds)
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- **Processing Rate**: 12.1 pairs/second
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- **Success Rate**: 100% (0 failed loads) |