How Should Platforms Label AI-Generated Content? Taiwan's Three-Layer Plan
Taiwan's Ministry of Digital Affairs (MODA) wants AI-generated content labeled in three layers: creators tick a box when they upload, platforms add a label automatically when they detect AI signals the uploader did not declare, and governments and users can report unlabeled content that gets through. It is a plan being negotiated with Meta, Google and LINE, not a law with stated penalties. Neither of the two reports we checked mentions fines.
The middle layer is the one most platforms will find hardest, because it depends on signals inside the file that a system can read and a person cannot see.
What exactly is Taiwan proposing?
According to Taiwan's Central News Agency and the Taipei Times, MODA has spent this year meeting Meta, Google, LINE, industry associations and experts about labeling AI-generated images and video. Tsai Shou-chuan, director of the ministry's Department of Digital Strategy, described the three layers on October 25.
The ministry is not asking platforms to use the same label position, size or design, but it wants labels to be easy to see. Tsai's example was YouTube, where viewers have to click "more" in the description to find the AI label; the ministry wants it placed right below the video. He said Google is moving faster while Meta is still making technical adjustments, and that for LINE, where most messages are private, labeling would start with public content such as ads.
Where AI-generated content is used in scams or infringes someone's rights, the ministry said it would work with platforms case by case on measures such as adding a label or removing the content.
How can platforms automatically flag AI-generated posts?
In MODA's plan, the automatic layer reads machine-readable markers that many AI tools already add. CNA quotes Tsai as saying that most AI-generated images carry hidden markers that people cannot see but systems can read. If a platform's scan finds those signs and the uploader did not tick the box, the platform adds the label itself.
In practice those markers come in two forms. One is provenance metadata, such as C2PA Content Credentials, which records how a file was made and is signed so that changes can be detected. The other is an invisible watermark mixed into the pixels or audio. The C2PA explainer states that metadata can be removed, and recommends pairing it with a soft binding such as a watermark or fingerprint so the record can still be found.
That is our reading of what the automatic layer will need, not a requirement the ministry has published: a label that depends on metadata alone can disappear when a file is screenshotted or passes through a step that strips metadata.
Why does each layer need the next one?
Each layer covers a gap the one before it leaves. The ministry itself says some posts may slip past the first two layers when creators remove embedded AI markers or when there is too much content to scan.
Layer | Who acts | What it relies on | Where it can miss | What catches the miss |
|---|---|---|---|---|
1. Self-label | Creator, at upload | A disclosure checkbox | Creator forgets or chooses not to disclose | Layer 2 |
2. Auto-detect | Platform, at scan | Hidden markers: provenance metadata, invisible watermarks | Markers stripped or damaged; volume too large | Layer 3 |
3. Report | Government, users, the person depicted | Reporting and privacy complaint channels | Content nobody reports | Not covered in the plan as reported |
Read across the rows, the weak point is the handoff from layer 2 to layer 3. The more often markers survive editing, the fewer cases have to wait for someone to notice and report them.
What should a platform or rights holder check now?
These questions come from the plan as reported.
Does your upload flow have a disclosure option, and is the resulting label visible without extra clicks?
Which signals can your system actually read at upload: C2PA Content Credentials, invisible watermarks, or neither?
What happens to those signals after your own transcoding, resizing or thumbnail step?
Is there a way for users, or the person shown in an image, to report an unlabeled AI file, and who reviews it?
MuseBlossom's Image Defence embeds an invisible watermark and issues and verifies C2PA Content Credentials, so a file can carry both kinds of marker the automatic layer looks for. Contents Defence appears on the C2PA Conforming Products List for image, audio and video. A listing confirms conformance with the specification, not how well a product works on your content, so test it on your own files.
For the European angle on who must label what, see our earlier post on EU AI Act Article 50 roles.
What doesn't this plan settle yet?
The reports describe no rule text, deadline or penalty, so platforms' obligations may change as talks continue. The plan also does not say how platforms should label AI content that carries no marker at all; detecting unmarked content relies on classifiers, which can misjudge human-made work. For LINE, private chats are out of scope at first.
FAQ
Is Taiwan's AI labeling plan a law?
Not as reported. It is a ministry plan being worked out with platforms, and the reports describe no fines.
What counts as a sign of AI use for automatic labeling?
The ministry pointed to hidden markers that systems can read. Provenance metadata such as C2PA Content Credentials and invisible watermarks are the two common types.
What if someone strips the marker before uploading?
That is what the third layer is for. Reports from the government, users or the person depicted let the platform add a label or take other action later.
Planning how AI labels will survive your upload pipeline? Ask us about Image Defence and C2PA Content Credentials.