Where does AI generated content moderation get hard?
Synthetic media becomes a moderation problem when it deceives, harms a person or breaks a platform rule. Trust and safety teams, marketplace integrity teams and community moderators see it in a handful of recurring forms.
- Marketplace listings with generated product photos that do not match the item sold.
- Fake profiles built on generated faces, used for spam, scams or influence operations.
- Fabricated “news” images and clips of public figures during elections or crises.
- Non-consensual intimate imagery made with face-swap or undressing tools.
- Generated reviews and comments in bulk, and links to scam storefronts built with AI.
What should an AI content labeling policy cover?
An AI content labeling policy should separate three cases: synthetic content that is disclosed, synthetic content that deceives, and synthetic content that harms someone. Each needs a different response.
| Case | Example | Typical response |
|---|---|---|
| Disclosed or provenance-marked | An illustration with C2PA credentials naming an image generator | Allow, with an automatic “made with AI” label |
| Undisclosed, low harm | AI art posted without a label in an art community | Label, and remind the user of the disclosure rule |
| Deceptive | A fabricated photo of a real event, a fake product photo | Label prominently, reduce reach or remove, depending on harm |
| Abusive | Intimate deepfake of a real person, impersonation for fraud | Remove fast, act on the account, support the victim |
Regulation is moving in the same direction. The EU AI Act, for example, includes transparency obligations for deepfakes and AI-generated content. Check the obligations that apply to your platform with counsel.
How does C2PA help detect AI-generated images on a platform?
C2PA content credentials are the strongest labeling signal because they are a statement attached to the file, not a statistical guess. When a file carries credentials naming an AI tool, you can label it with confidence. Read what C2PA content credentials are for the details.
The catch is that credentials are easy to lose. Many upload pipelines strip metadata when they resize or recompress images. Read and store provenance at ingest, before any processing. And remember that missing credentials prove nothing: most real photos carry none either. A content credentials checker helps reviewers inspect a single file.
AI Photo Detector · Free plan
Check an image for content credentials, AI-image signals and visible anomalies, with the reasons listed for your reviewers.
A deepfake moderation workflow that keeps humans in charge
Good deepfake moderation triages by harm first and uses synthetic media detection to inform reviewers, not to replace them.
- 01Write the policy tiers above and publish them, so users know what gets labeled and what gets removed.
- 02Read C2PA and EXIF/XMP data at upload and apply labels automatically when provenance names an AI generator.
- 03Triage reports by harm: intimate imagery, impersonation and fraud first, regardless of how sure you are that the content is AI.
- 04Give reviewers detection results on escalated items, with the listed signals and explanation, alongside account history and context.
- 05Make the decision human, record the reasons and offer an appeal. Detectors are wrong in both directions.
- 06Review a sample of decisions regularly to see where detection helped and where it misled, and adjust thresholds.
For reviewer training, the guides on how to tell if an image is AI-generated and how to spot a deepfake video make a good starting curriculum.
How reviewers should read a detection result
Give reviewers a short guide so the same result leads to the same decision across the team:
- Probably AI: a strong signal. Check the listed reasons, then apply the policy tier for that content. Provenance naming a generator is the most solid reason.
- Uncertain: no information either way. Decide on context, account history and reports, as you would without a tool.
- Probably human: not a clearance. A real photo with a false caption is still misleading.
- Mixed (text and websites): only some passages or pages look generated. Look at which ones before acting.
- Attach the PDF report to the case for appeals and audits, and use the “the result is wrong” button when you know better.
Detecting generated reviews, posts and comments
Generated text is the hardest synthetic media to moderate, because short posts carry little signal and many people now write with AI help for legitimate reasons. Behavior usually gives coordinated campaigns away before the words do.
- Many new accounts posting similar reviews in a short window, often for the same product or seller.
- Reviews that describe features in catalog language but never mention using the product.
- Identical structure across posts with only names and details swapped.
Use the AI text detector on longer posts (at least 300 characters) as one input. A generated review is not automatically a fake review; whether the person bought and used the product is what matters.
Which AnyFact detectors fit trust and safety teams?
Five detectors cover the content types platforms host. Each returns a verdict (probably human, uncertain or probably AI; text and websites can also be mixed), the signals behind it, a plain-language explanation, the limits of the method and a PDF report for the case file.
| Content | Detector | What it returns |
|---|---|---|
| Images, profile photos, listings | Photo | Provenance findings, a classifier verdict and visible anomalies |
| Video clips | Video | A timeline of suspicious moments, a face-swap check when a frontal face is clear, audio checked as voice |
| Audio clips | Voice | A score per segment, a transcript and scam-pattern flags |
| Posts, reviews (300+ characters) | Text | A verdict per passage, with generated passages highlighted |
| Linked external sites | Website | Up to 20 pages checked, domain age and Google Safe Browsing where available |
An honest note on fit: AnyFact has no public API yet and works through its web app. It suits escalation review, appeals and policy calibration on samples. For automated scanning at platform scale, you need an API-based provider today.
AI Website Checker · Free plan
Check a linked storefront or news site for generated pages and images, with domain age and safety signals.
Limits of synthetic media detection at scale
Detection is weakest on the content platforms see most: compressed, cropped, screenshotted and re-uploaded files, and very short texts. Text detectors also misread non-native writers more often, which matters for global platforms. A policy that removes content on a detector score alone will produce unfair takedowns. Read how accurate AI detectors are before setting any automated threshold.
Frequently asked questions
Should platforms remove all AI-generated content?
No. Most AI-generated content is harmless, and much of it is clearly creative. A better policy labels synthetic media and removes content that deceives or harms people, whatever tool made it.
Does AnyFact have an API for content moderation?
Not yet. AnyFact works through its web app, which fits reviewer escalations, appeals and policy research rather than scanning every upload automatically.
Is C2PA enough to label AI content?
It is the most reliable signal when present, but many files carry no credentials and many pipelines strip them. Use C2PA for automatic labels and detection plus user reports for the rest.
How should we handle intimate deepfakes reported by a victim?
Act on the report quickly and remove the content. Whether a detector rates it as AI does not change the harm, so do not make the victim wait for a detection result.
Can AnyFact check a whole website linked from our platform?
Yes. The website checker explores up to 20 public pages, respects robots.txt, checks texts and images, and adds domain age and Google Safe Browsing results where available. Pages behind a login are not analyzed.