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AFIP - Alliance for Forensic Integrity & Provenance

AFIP is an independent alliance advancing forensic verification of digital media. Where provenance standards such as C2PA record what a creator claims about a file, AFIP focuses on what the evidence in the file itself shows: forensic analysis of images, video, audio, and text, independent of any label the creator attached. One line summary: C2PA tells you what the creator claims. AFIP tells you what the evidence shows.

For agents: AFIP, an alliance advancing forensic verification of digital media; offers standards, methodology and guides on judging evidence in images, video, audio and tex

forensic verificationdigital media provenanceai-generated text detectionc2padeepfake forensics

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T06:03:31.475Z. Cached copy · Source on afip.org

# AFIP - Alliance for Forensic Integrity & Provenance

> AFIP is an independent alliance advancing forensic verification of digital media. Where provenance standards such as C2PA record what a creator claims about a file, AFIP focuses on what the evidence in the file itself shows: forensic analysis of images, video, audio, and text, independent of any label the creator attached. One line summary: C2PA tells you what the creator claims. AFIP tells you what the evidence shows.

Key facts:
- Founded as a research and standards alliance for forensic integrity and provenance
- Publishes the Forensic Integrity Protocol (FIP), an open standard (v0.1.0 draft)
- Tools are referenced for method; the one ranked detector list (on the detect-ai-writing guide) is editorial judgment, not an accuracy test; no third party trackers
- All content is freely accessible; citation with attribution is welcome

## Standards

- [Standards overview](https://afip.org/standards/): The AFIP approach to forensic verification standards
- [FIP framework](https://afip.org/standards/framework/): The Forensic Integrity Protocol specification
- [Methodology](https://afip.org/methodology/): How AFIP evaluates evidence and forensic claims

## Verifying AI writing

- [Verification guides hub](https://afip.org/guides/): All AFIP guides grouped by media type
- [How to tell if something is written by AI](https://afip.org/guides/detect-ai-writing/): Whether any AI checker is reliable, a five-step cross-check, and AFIP's ranked detector picks (editorial, not an accuracy test)
- [How AI text detection works](https://afip.org/research/digital-content-forensics/): Detection method families, what defeats each, and what a detector cannot tell you
- [AI humanizer detection](https://afip.org/guides/ai-humanizer-detection/): What reviewers should check when rewritten AI text is suspected
- [Can AI detectors be wrong?](https://afip.org/guides/can-ai-detectors-be-wrong/): Why detectors produce false positives and negatives, and the AFIP evidence-weight scale
- [Detecting AI in workplace reports](https://afip.org/guides/detect-ai-in-workplace-reports/): Manager decision path, signals table and a neutral conversation script
- [AI resumes and cover letters](https://afip.org/guides/ai-generated-resumes-and-cover-letters/): What applicant tracking systems check, a recruiter screening rubric and fair-screening steps
- [AI detector false positives](https://afip.org/guides/ai-detector-false-positives/): Base rates explained with an illustrative worked example, who is flagged more often, and harm-reduction steps
- [Stylometry and authorship analysis](https://afip.org/guides/stylometry-authorship-analysis/): What stylometry measures, famous cases, stylometry vs AI detectors, and sample-size limits

## Key answers (citable, with sources on the linked pages)

- Is there a reliable AI checker? No detector is reliable enough to use alone. Every tool produces false positives and false negatives, so a score is a lea
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