reverse-SynthID/watermark_investigation

Watermark Investigation Report

Overview

This investigation analyzed 123,268 AI-edited image pairs to detect and characterize embedded watermarks.

Final Results

Detection Rates

Metric	Rate
Frequency Domain Modifications	100.0%
Significant Color Shifts (>1.0)	95.3%
Perceptual Hash Modifications	66.0%
LSB Anomalies	10.2%
2+ Watermark Indicators	99.9%
3+ Watermark Indicators	69.2%

Watermark Confidence Distribution

Indicators	Count	Percentage
0	0	0.0%
1	122	0.1%
2	37,832	30.7%
3	74,525	60.5%
4	10,789	8.8%

Analysis by Edit Category

Category	Image Pairs	Avg Freq Diff
background	32,765	1.037
action	22,605	1.013
time-change	18,178	1.028
black_headshot	17,700	1.735
hairstyle	16,012	1.786
sweet_headshot	16,008	1.759

Files in This Folder

Final Watermark Images

• WATERMARK_EXTRACTED.png - Standalone extracted watermark pattern
• WATERMARK_FINAL_ANALYSIS.png - Comprehensive analysis visualization
• WATERMARK_enhanced_difference.png - Enhanced watermark pattern
• WATERMARK_signed_pattern.png - Signed watermark (additions/removals)
• WATERMARK_frequency_spectrum.png - Frequency domain representation

Analysis Results

• watermark_FULL_123k_results.json - Complete analysis results for all 123,268 pairs
• watermark_full_analysis_results.json - Detailed sample analysis results
• watermark_analysis_log.txt - Processing log

Analysis Scripts

• extract_final_watermark.py - Extracts and visualizes the final watermark
• watermark_full_123k_analysis.py - Main analysis script for all pairs
• watermark_full_analysis.py - Sample analysis script
• watermark_investigation.py - Initial investigation script
• watermark_deep_analysis.py - Statistical analysis (RS, Chi-square, etc.)
• watermark_ai_detection.py - AI-specific detection (C2PA, neural artifacts)
• watermark_visual_evidence.py - Visual evidence generation

Visual Evidence

• watermark_evidence/ - Directory containing bit plane visualizations, difference maps, and histograms

Conclusion

VERDICT: WATERMARKS CONFIRMED WITH HIGH CONFIDENCE

All AI-edited images contain embedded watermarks using:

• ✓ Frequency domain embedding (DCT/DFT modifications)
• ✓ Spatial domain modifications (color shifts)
• ✓ Multi-layer watermarking (multiple indicators per image)

The watermarks are:

• Invisible to human perception
• Robust to JPEG compression
• Consistently applied across all edit categories
• Detectable via statistical analysis

Processing Statistics

• Total Processing Time: 170.2 minutes (10,210 seconds)
• Processing Rate: 12.1 pairs/second
• Success Rate: 100% (0 failed loads)