Synthetic Media Policy: Put C2PA, FTC Rules, and Takedowns to Work
A synthetic media policy has to require provenance metadata, visible disclosure, documented consent, human review, a notice-and-removal workflow, and a named owner who answers for all of it. The C2PA specification for provenance, the TAKE IT DOWN Act for nonconsensual intimate imagery, and the FTC final rule on fake reviews and testimonials anchor that work. We build the rest of this guide around them.
TL;DR:
Covered platforms need a published notice form, trained reviewer, duplicate search, and vendor response terms that meet the 48 hour removal deadline.
Marketing rules must prohibit fake reviews, testimonials, and follower signals created with AI; disclosure labels do not cure those deceptive practices.
C2PA credentials record an asset’s origin and edits and expose tampering, but they cannot establish whether the depicted event actually happened.
Label fully generated media on the asset itself, and disclose partial edits whenever changed words, faces, or settings could alter its meaning.
Require signed releases that explicitly cover AI use of a person’s likeness and voice, and require human approval for intimate content and advertising claims.
Table of Contents
What should a synthetic media policy cover, and why does provenance come first?
A good policy starts with a plain definition. Synthetic media is any image, video, audio or text that an AI system generated, materially modified or stitched together with other material. That includes fully generated assets, AI-edited footage, voice clones, face swaps and composites where a generated element sits inside a real shot. The policy should name every asset type your team publishes and every channel where it appears, from campaign video to social cutdowns to archived masters.
Provenance is the organizing principle because it solves several problems at once. A signed record of how an asset was made tells your audience what they are looking at, tells your reviewers what to check, and tells your auditors what happened. The C2PA standard calls this record a Content Credential: a cryptographically signed manifest bound to the file. We treat it the way a film production treats a call sheet. It does not make the work good, but without it nobody can reconstruct who did what.
Provenance also maps cleanly onto the goals a policy exists to serve:
Consent: the manifest and your release paperwork together show whose likeness or voice was used and under what terms.
Attribution: the credential names the tools and actors involved, so credit and accountability follow the asset.
Traceability: every edit that touches the file is recorded in order, which turns a vague “who changed this?” into a checkable answer.
Reduced harm: when something goes wrong, you can find the original, find the copies and respond quickly.
Partnership on AI makes the same argument from the ethics side: its responsible practices for synthetic media center on consent, disclosure and transparency rather than blanket suppression. That is the posture we recommend. Write the policy to govern how synthetic media is made and shown, not to pretend it can be avoided.
Which U.S. laws and rules shape your policy obligations?
Federal measures shape obligations for organizations that publish or host content, and another development affects how you keep evidence.
The first is the TAKE IT DOWN Act, enacted as Public Law 119-12 on May 19, 2025. It creates criminal prohibitions for certain disclosures of nonconsensual intimate visual depictions, including digitally forged ones, and requires covered platforms to run a notice-and-removal process. The statutory text, available as the enrolled bill, defines digital forgery and lists the elements a valid removal request must contain.
Covered platforms must remove nonconsensual intimate imagery no later than 48 hours after receiving a valid notice. That deadline is the operational number your workflow has to be built around.
The second is the FTC’s final rule on consumer reviews and testimonials. According to the FTC announcement, the rule prohibits using generative AI to create or spread fake consumer reviews and fake indicators of social media influence. It reaches both AI-generated and human-generated deception, so scaling a deceptive practice with AI does not shield anyone. The full rule text targets unfair or deceptive commercial conduct, which matters if your team produces testimonials, influencer content or review-style assets.
Here is what that means for your drafting:
Your policy needs a notice-and-removal clause if you host user content, and the clause must match the 48-hour timeline.
Your marketing rules must ban synthetic testimonials, invented reviews and purchased or generated follower signals.
Labeling alone does not satisfy either obligation. Metadata is valuable, but it is not a substitute for meeting a specific statutory duty.
Preserve originals, logs and manifests for anything that could end up in a dispute.
How do C2PA Content Credentials work in practice?
You do not need to be an engineer to write sound provenance requirements, but you do need to know what the standard can record. The C2PA specification describes cryptographically signed manifests that travel with an asset. The C2PA implementation guide explains how teams deploy them. These are the pieces your policy should reference:
digitalSourceType: an IPTC field that states how the asset was made. The value TrainedAlgorithmicMedia marks content produced by a trained model, and other values distinguish partial AI involvement.
c2pa.ai-disclosure assertion: a structured statement inside the manifest that discloses AI use in a form software can read.
Regions of Interest: markers that point to the specific part of an image or video that was generated or altered, so a label can say “this area” instead of condemning the whole frame.
Generation recipe via inputTo: a record of which inputs fed into a generation step, which supports audits of consent and licensing.
Hard and soft bindings: the links that tie a manifest to its file so tampering becomes detectable.
Be exact about what provenance proves. According to C2PA’s own white paper, Content Credentials record well-formed provenance and make tampering evident, but they do not assert that the content is true. A signed manifest tells you where an image came from and what was done to it. It cannot tell you whether the scene it depicts really happened.
The policy action item is simple: require that manifests are attached at publish and preserved at every archival point.
How should you label and disclose synthetic media?
Layer your disclosure. A machine-readable credential serves software and auditors, a visible label serves the viewer, and editorial copy or a caption serves context. Any one layer can fail, such as a platform stripping metadata, so the others carry the load.
Use these decision rules:
Label fully generated assets every time. If a model produced the image, voice or footage, say so on the asset itself, not only in a footnote.
Label partial edits when the change could alter meaning. Retouching skin tone is routine. Replacing a speaker’s words, face or setting is not, and it earns a label.
Handle satire and commentary deliberately. Obvious parody still benefits from a disclosure line when it could circulate without context, so default to labeling and let an editor approve any exception in writing.
Word labels plainly. “Generated with AI” or “AI-edited: background replaced” beats vague phrases such as “enhanced” or “digitally created.”
Place labels where eyes land. Put them on the media or directly beneath it, never buried in a profile bio.
Surface Regions of Interest. When the credential marks which area was generated, show that to the viewer so the label stays honest about scope.
Keep the provenance record. Retain manifests and edit logs for as long as the asset lives, and longer if a dispute is possible.
Google’s guidance points the same way: its search documentation treats AI content as acceptable when it meets quality and policy standards, and it recommends human review and metadata to preserve audience trust.
What must platforms and publishers build into notice-and-removal workflows?
If you host or distribute user content, the 48-hour rule turns policy language into a stopwatch. The workflow has to be documented, staffed and testable before the first notice arrives.
Build these elements:
A clear submission form. Make it easy to find and fast to complete, with fields that mirror the elements the statute requires in a valid request.
Evidence and identity requirements. Ask for enough to confirm the requester is the person depicted or an authorized representative, without creating barriers that discourage real victims.
A published timeline. State your removal commitment publicly and keep it consistent with the 48-hour requirement.
Triage and verification. Assign a trained reviewer to assess each claim, capture a snapshot of the content and its URLs before removal, and search your own services for duplicates and re-uploads.
Vendor and moderation-partner terms. Contractually require response SLAs that fit inside your own deadline, a defined escalation path, and retention rules that protect evidence while limiting who can view sensitive material.
Rehearse the process. Submit a test notice periodically and measure how long it takes to reach a decision.
What belongs in a practical drafting checklist?
Use this sequence when you write or revise the policy. It works whether you are a studio, an agency or an in-house brand team.
Set scope and ownership. Define synthetic media, list covered assets and channels, and name one accountable owner with legal, creative and operations sign-off.
Write the consent clause. Require signed releases that explicitly address AI use of likeness and voice. Our guide to model release clauses for AI and ads covers the language worth including.
Mandate provenance. Require Content Credentials at export, preservation at archive, and a prohibition on stripping manifests.
Fix labeling rules. Adopt the layered disclosure approach and the decision rules above.
Require human review. Name the asset categories that cannot publish without a person’s sign-off.
Document notice-and-removal. Include the submission path, the 48-hour commitment and the escalation owner.
Define retention and audit logs. State how long manifests, review records and takedown files are kept and who may access them.
State enforcement. Spell out consequences for staff, contractors and vendors who break the policy.
Attach the operational annex. Include a sample takedown notice, the required metadata schema, an escalation matrix, and a training and communications plan.
For licensed audio, tie the policy to your rights process too; our walkthrough on licensing AI music for campaigns shows where consent and licensing meet.
How do you put the policy into production with the right tools and human review?
A policy that lives in a PDF changes nothing. Wire it into the pipeline.
Attach, preserve and verify. Configure your editing and export tools to write C2PA manifests, check them again at publish, and block assets whose credentials fail validation.
Log immutably. Store provenance records and review decisions in an append-only log so nobody can rewrite history after an incident.
Define mandatory human sign-off. Require a named reviewer for sensitive subjects, real people’s likenesses, celebrity or public-figure imagery, anything intimate, and any asset used in advertising claims. We outline workflows that keep people in charge in our post on AI workflows for video teams, and a brief-first approach to sign-off in our content review workflow.
Preserve authorship. When AI assists with text or copy, keep a record of what the human wrote and what the tool suggested; this three-pass proofreading approach shows one way to keep the writer’s voice intact.
Set a governance cadence. Audit manifests on a sample of published work, train staff twice a year, run a tabletop exercise on a takedown scenario, and publish a transparency report.
What do you do when synthetic media causes harm?
Speed and care matter equally. Work in this order:
Remove and preserve. Take the content down inside the statutory window, but first capture the asset, its URL, its manifest and the notice itself.
Notify and escalate. Tell the affected person what you did, bring in counsel, and involve law enforcement where the situation warrants it.
Check the copies. Search your services and partners for duplicates and re-uploads, and document what you found.
Communicate without amplifying. Do not repost or describe the harmful material in detail. Use victim-centered language, state what you did and what happens next, and avoid speculation about motive.
Close the loop. Review how the content got through, then update the policy and the annex.
Why provenance beats prohibition
Our view, plainly: the loudest policy debates fixate on detection and bans, and both disappoint. Detection tools are fallible, and a ban just pushes work into places where nobody records anything. Provenance gives you something sturdier, a paper trail that makes honest work easy to prove and dishonest work easy to question. Creative teams lose little to this. Flexibility survives when the rules are about disclosure and consent rather than about which tools are allowed. The cost is discipline at export and archive, and that is a habit, not a creative constraint. Where policy tends to fail is the gap between what it promises and what a tired team does at 11 p.m. before a deadline, so we write rules a production schedule can actually keep.
— Vain.
How we help you build and audit a synthetic media policy
We work with brands, artists and organizations that want these controls built into the way they make things, not stapled on afterward. Our team can help with:
Producing video, audio and animation.
Developing marketing strategy, brand development and audience growth solutions.
If you want a partner who understands both the craft and the compliance, talk to us about our video, audio, animation and strategy services and tell us where your current process feels thin.
FAQ
What is an example of synthetic media?
A voice clone that reads a script in someone’s voice is a common example. So are AI-generated images, face-swapped video and composite footage where a generated element sits inside a real shot.
Are deepfakes actually illegal in the U.S.?
It depends on the content. Federal law now criminalizes certain disclosures of nonconsensual intimate imagery, including digital forgeries, under Public Law 119-12. Other deepfakes, such as political or commercial ones, fall under a patchwork of state and federal rules, so check a lawyer for your specific case.
In what states are deepfakes illegal?
We cannot give a reliable state-by-state list here, because state laws differ in scope and change often. Confirm current statutes for every state where you publish or host content.
Can you report AI videos on YouTube?
Use the platform’s in-product report function and keep a copy of your submission. Covered platforms must offer a notice-and-removal route for nonconsensual intimate imagery, with removal within 48 hours of a valid notice.
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