wu-forensics 1.1.0

Epistemic media forensics toolkit - structured uncertainty for courts

Wu - Epistemic Media Forensics Toolkit

Detects manipulated media with structural uncertainty output. Hopefully it will be suitable for court admissibility (Daubert standard).

Developed by Zane Hambly.

Named after Chien-Shiung Wu (1912-1997), who disproved parity conservation and found asymmetries everyone assumed didn't exist.

Installation

pip install wu-forensics

Quick Start

# Analyse a photo
wu analyze suspicious_photo.jpg

# JSON output
wu analyze photo.jpg --json

# Batch analysis
wu batch *.jpg --output reports/

Python API

from wu import WuAnalyzer

analyzer = WuAnalyzer()
result = analyzer.analyze("photo.jpg")

print(result.overall)  # OverallAssessment.NO_ANOMALIES
print(result.to_json())

Detection Dimensions

Wu analyses images across multiple forensic dimensions:

Dimension	What It Detects
metadata	Device impossibilities, editing software, AI signatures, timestamp issues
visual/ELA	Error Level Analysis - compression inconsistencies from splicing
quantization	JPEG quality table mismatches between image regions
copy-move	Duplicated regions within the same image
PRNU	Photo Response Non-Uniformity - sensor fingerprint anomalies
lighting	Inconsistent light direction across image regions
blockgrid	JPEG block boundary misalignment

Benchmark Performance

Tested on standard forensic datasets (CASIA 2.0, CoMoFoD):

CASIA 2.0 (Splice Forgeries)

Dimension	Precision	Recall	FPR
quantization	95%	39%	2%
visual/ELA	91%	41%	4%
prnu	67%	6%	3%
copy-move	57%	47%	36%
lighting	57%	64%	48%

Combined Detection

Strategy	Precision	Recall	FPR	Use Case
ELA + Quantization	91%	41%	4%	Conservative/Legal
All dimensions	57%	90%	67%	Screening

Key finding: ELA + Quantization provides 91% precision with only 4% false positive rate on splice forgeries.

CoMoFoD (Copy-Move Forgeries)

Copy-move within the same image is harder to detect (identical compression/quality):

Dimension	Precision	Recall	FPR
prnu	61%	38%	24%
copy-move	50%	68%	68%

Note: CoMoFoD includes "similar but genuine objects" designed to challenge detectors.

Epistemic States

Unlike binary classifiers, Wu reports structured uncertainty:

State	Meaning
CONSISTENT	No anomalies detected (not proof of authenticity)
INCONSISTENT	Clear contradictions found
SUSPICIOUS	Anomalies that warrant investigation
UNCERTAIN	Insufficient data for analysis

Court Admissibility

Wu is designed with the Daubert standard in mind:

1. Testable methodology: Every finding is reproducible
2. Known error rates: Confidence levels are explicit
3. Peer review: Academic citations throughout
4. General acceptance: Based on EXIF standards (JEITA CP-3451C)

References

• Wu, C.S. et al. (1957). Experimental Test of Parity Conservation in Beta Decay. Physical Review, 105(4), 1413-1415.
• Farid, H. (2016). Photo Forensics. MIT Press.
• JEITA CP-3451C (Exif 2.32 specification)
• Daubert v. Merrell Dow Pharmaceuticals, 509 U.S. 579 (1993)
• Wen, B. et al. (2016). COVERAGE - A Novel Database for Copy-Move Forgery Detection. IEEE ICIP.
• Dong, J. et al. (2013). CASIA Image Tampering Detection Evaluation Database. IEEE ChinaSIP.

AI Usage

This project uses Claude (Anthropic) to assist with summarising test results across 700+ test cases. All code, forensic methodology, and documentation are human-authored by me.

License

MIT