Open Standard
Forensic Integrity Protocol
An open standard for independent verification of digital media authenticity. Privacy-preserving. Platform-agnostic. Adversarially robust.
Current media provenance standards rely on a fundamental assumption: that content creators will voluntarily attach cryptographic metadata declaring how their content was made. This self-labeling approach has three structural weaknesses that no amount of adoption can fix.
First, metadata is routinely stripped during distribution. Social media platforms recompress uploads, removing embedded provenance data. A photograph with a valid content credential loses its label the moment it's shared on Instagram, X, or TikTok. The credential exists at creation and vanishes at the point of consumption—precisely where verification matters most.
Second, self-labeling proves history, not truth. A content credential attests that a specific entity claims to have created or modified content in a specific way. It does not prove the content is authentic or unmanipulated. The distinction between "this person says they made this" and "this content has not been synthetically generated" is the difference between a self-declaration and a forensic analysis.
Third, current standards embed creator identity into content metadata, creating documented privacy and doxing risks for journalists, activists, and creators in hostile environments. Independent verification should not require sacrificing anonymity.
The AFIP Forensic Integrity Protocol takes a fundamentally different approach. Rather than asking creators to label their own work, FIP enables independent third-party verification of any digital content—with or without the creator's cooperation, with or without embedded metadata, and without exposing creator identity.
FIP operates through three complementary mechanisms: perceptual fingerprinting that survives platform processing, multi-modal forensic analysis that detects synthetic generation and manipulation, and cryptographic attestation that produces verifiable forensic reports. Together, these mechanisms provide what self-labeling cannot: evidence-based verification that works across the entire content lifecycle.
Content-derived hashes that survive compression, cropping, format conversion, and screenshot capture. Register a fingerprint at creation; recover it anywhere in the distribution chain.
Detection engines for text, image, audio, and video that identify synthetic generation, splicing, cloning, and manipulation through statistical and structural analysis.
Cryptographically signed analysis reports in machine-readable format. Verifiable by any third party without trusting the analyzer—trust the evidence, not the institution.
Decentralized fingerprint registry enabling content lookup across the distribution chain. Find the provenance of any content, even after metadata has been stripped.
The Forensic Integrity Protocol is currently in active development. The specification is being drafted as an open document with RFC-style versioning. Reference implementations will be published under permissive open-source licenses.
| Component | Status | Target |
|---|---|---|
| Perceptual Fingerprinting Spec | Draft | Q2 2026 |
| Multi-Modal Analysis Protocol | Draft | Q2 2026 |
| Attestation Format | Planning | Q3 2026 |
| Registry Protocol | Planning | Q3 2026 |
| Reference Implementation | Planning | Q4 2026 |