Photographers
Your portrait, re-uploaded without your terms attached.
Even after it's cropped or re-uploaded.
Say whether your photos, videos, or audio can be used for ads, AI training, or commercial work. Register once — check anytime, straight from the file itself.
A photo, video, or audio clip — straight from your device.
Viewing only? Ads? AI training? Pick one of four presets, or set your own.
Even after it's cropped, recompressed, or re-uploaded somewhere else.
Follow a single illustrative file through its whole lifecycle — registered, chained, shared, checked, and withdrawn.
Illustrative walkthrough using a stock photo, not a live verification result. Matching depends on the file and the edits made to it; a fingerprint match is not a guarantee that every altered copy can be identified.
Any file that leaves your hands can lose its terms along the way. These are the four we hear about most.
Your portrait, re-uploaded without your terms attached.
A clip lifted into someone else's track, credit stripped.
A face used to train a model you never agreed to.
Work scraped into a dataset with the byline removed.
Private only — nobody else should display, share, or train AI on this.
Anyone can view and share it. Not for ads, AI training, or resale.
Approved platforms can use it commercially — you get paid automatically.
Open for anyone to use, remix, or train AI on.
Set monetization and AI training independently instead of picking a preset combination — e.g. non-monetized but AI-trainable, a combination none of the four presets cover.
Setting terms during registration looks like this — four preset consent levels plus independently configurable custom terms, each a W3C ODRL policy signed with a wallet or email-derived key.
01 / 04 · Set terms, sign them
The rights holder sets terms — commercial use, AI training, derivative works, monetization split — as a W3C ODRL policy, then signs it with a wallet or email-derived key. Fingerprints are computed from the file itself, client-side.
Result: the file, its signer, and the signed terms are now linked together.
Every action — registration, amendment, revocation — is appended as a SHA-256-linked block in a real Firestore-backed ledger, then anchored to the public Sigstore Rekor transparency log: an independent, permanent record outside ProveConsent's own database.
Result: every later action inherits this block's hash — earlier history can't be quietly edited without breaking the chain.
An uploaded copy is resolved back to its original registration by what it is — perceptual and content-derived fingerprints — not by filename, embedded tags, or metadata a platform may have already discarded. 95–100% of lightly re-encoded or resized copies, 100% of heavily cropped or recompressed ones, 0% false matches across unrelated images in the same test.
Result: a copy resolves back to this record by its content, not its filename.
Consent can be withdrawn at any time. The revocation is itself a permanent, timestamped ledger entry — never a silent deletion of the record.
Result: the withdrawal itself becomes a new, permanent entry — never a deletion of what came before.
A manifest travels attached to the file, so it's gone the moment a platform strips it or a screenshot is taken. ProveConsent is built to complement that approach, not replace it — resolution and revocation status come from the content itself, so the record is still reachable after the manifest isn't.
Measured directly against this app's own production matching code (real perceptual hashing and real content-derived embeddings — never a simulated stand-in) across 7 real photographs and 10 realistic transformations (JPEG recompression, resize round-trips, center crops, and brightness/contrast shifts, at light/moderate/heavy severity), plus every cross-pair among those 7 photos for the false-match figure. A small sample from an early-stage product, not a large-scale study — re-measured whenever the matching code changes.
Every new registration also stores a crop-alignment set (several simulated crop levels of the original) and 9 overlapping tile fingerprints, used automatically during verification alongside the plain fingerprint check above — this is what lifted heavy-crop matching from 80–90% to the 100% figure above. It also separately catches 14–100% of tight, off-center crops (e.g. zoomed into just one detail of a photo) depending on how closely the crop lines up with a stored region — averaging around 40% across the 5 crop positions tested, with no change to the 0% false-match rate. Applies to assets registered from this point forward; not retroactive, since original files aren't retained after registration.
Register a file, set its terms, and know the record is still reachable — long after the metadata isn't.