Proving a photo is real when anyone can edit one

A photograph used to be evidence. With generative editing, a face can be replaced, an object removed or a document altered in seconds, and the result survives casual inspection.

The highest-stakes version is the face morph: two people's photos blended into one image that resembles both. Printed in a passport or ID card, it can let two people use one document. NIST runs a dedicated evaluation of morph detection algorithms because border and identity agencies treat it as a real threat.

The problem: detection alone is an arms race

Detectors that spot manipulation work by learning the traces editing tools leave. Each new generation of tools leaves fewer, so detection accuracy decays and must be retrained. A detector also answers only "does this look edited?", never "is this the original?"

The proposal: provenance first, detection as backup

We sketched this as UnMorph, an image integrity layer, currently a front-end prototype with a real fingerprinting step and simulated registry and analysis. The design has three steps:

1. 01Register. Fingerprint the original twice: a cryptographic hash (SHA-256) that changes if a single pixel changes, and a perceptual hash that survives resizing and recompression. Optionally bind the registration to a verified unique human using a privacy-preserving proof of personhood, so one person cannot flood the registry.
2. 02Anchor. Write the fingerprints and metadata to a tamper-evident, owner-controlled record, and store the original and its evidence bundle in durable storage. Anyone can later check what was registered and when.
3. 03Verify. Check any copy against the registry. An exact hash match proves it is the original. A perceptual match with a different hash means it is a derivative, and region-level tamper detection shows where it was altered, with a confidence score that can be audited.

Where standards fit

The C2PA standard and its Content Credentials let cameras, editing tools and platforms attach signed provenance to media: who captured it, with what, and what edits were made. It is the right foundation for content created from now on. A registry like the one above covers what C2PA does not: images whose credentials were stripped, images from before credentials existed, and identity documents where an issuer needs to check a photo against what was enrolled.

Who needs this

• Identity and KYC providers: check enrolment photos for morphing and match later submissions to what was enrolled.
• Insurers and claims teams: spot edited damage photos and reused images.
• Newsrooms and platforms: show provenance for published media and flag stripped or altered copies.
• Legal teams: preserve evidence with a verifiable chain of custody.

What to measure

• Coverage. The share of originals fingerprinted at the moment they first enter your workflow.
• Match accuracy. How often verification correctly identifies an original, a legitimate derivative and an altered copy, on a labelled test set.
• Detection on unregistered images. Tamper-detection precision and recall on images without provenance, retested as new editing tools appear.
• Time to verify. How long a reviewer waits for a verdict they can defend.

How we would start

Decide where in your workflow an original first appears, at capture, upload or enrolment, and fingerprint it there; everything else depends on that moment. We build these pipelines as part of our document AI and custom model work, with onchain anchoring from our Web3 practice where a public, tamper-evident record helps.