EU AI Act: Marking AI-Generated Images | On-Model Blog<br>Back to BlogNext: Virtual Try-On for Fashion Brands

AI now produces a large share of the product imagery in fashion e-commerce, and with that shift comes a fair question from shoppers, marketplaces, and regulators alike: can you tell what came from a camera and what came from a model? The European Union has answered it with a rule. From 2 August 2026 , any system that generates or substantially edits synthetic images has to mark its output so that software can tell it was made by AI.

For a fashion brand, this is not an abstract compliance headache. It is a question your legal or procurement team will ask before they sign off on any tool that touches your catalog: "If we publish these images, are they labelled the way the law expects?" We think the honest answer a vendor should be able to give is a simple one. Yes, and you did not have to do anything to make it happen.

This post covers what the EU AI Act's transparency rules actually ask for, what On-Model now does automatically on every image, and where your responsibility as a brand begins.

What the EU AI Act actually asks for

The relevant part of the law is Article 50. It sets out two duties that matter here.

The first is a provider duty. Whoever builds the AI system that generates or substantially alters an image must mark the output in a machine-readable format, so that it is detectable as artificially generated. On-Model is that provider.

The second is a deployer duty. Whoever publishes an image that qualifies as a deep fake must disclose that it is artificial. When you publish images downstream, that role is yours.

The phrase to hold onto is machine-readable. It does not mean a visible logo stamped across the picture. It means information that travels with the image file, in a place people rarely look but software always can, so that a platform, a browser, a marketplace, or a verification tool can read it and know the image was AI-generated. In June 2026 the European Commission published a Code of Practice on marking and labelling AI-generated content that spells out what "good" looks like: standardized metadata first, with more durable layers recommended on top. The Code is voluntary, but it is the blueprint regulators will measure against.

There is a narrow exception for "assistive" edits that do not substantially change an image. Swapping the model in a photo, or generating a person onto a flat-lay garment, is not that. We treat our outputs as in scope, which is the safe and honest reading.

How On-Model marks AI-generated content

Every image On-Model produces is marked as AI-generated in a machine-readable format, automatically, on every plan. You do not switch it on, and there is no way to accidentally turn it off.

Whichever tool you use, whether Model Swap, Flat-to-Model, Create Packshot, or any of the others, the finished image leaves the platform carrying a standardized marker that identifies it as artificially generated. The marker uses a value defined by a recognised industry standard, not a private flag only we can read, so any tool that knows how to look for AI-provenance information can find it. That is what makes it interoperable, which is exactly what Article 50 asks for.

It covers the whole surface, not just the first attempt. First generation, re-runs, regenerated variants, and post-processed versions all carry the marker. Free plans are included. That last point was a deliberate decision: the law does not have a free tier, so neither does our marking. An image made on the free plan leaves marked in the same way an enterprise image does.

Note what we chose not to do. We did not make this a paid add-on or an enterprise-only toggle buried in settings. Compliance that someone has to remember to enable is compliance that eventually fails in production. Making the marker unconditional is the only version of this that actually holds up at catalog scale.

Why marking at the source matters

On-Model is less a creative app you open to make one image, and more a piece of infrastructure your catalog flows through. The useful comparison is Stripe rather than Photoshop: product catalog in, processed imagery out, on into your storefront, your marketplaces, and your ads. The AI is one step inside a larger system.

Because the marking happens inside that pipe, at the exact point the image is made, every brand publishing through On-Model inherits it. You do not add a compliance step to your workflow. You do not train your team to remember it. You do not audit each image by hand. The label is already applied before the file ever reaches you. Compliant by default.

That is the difference between a rule you have to operationalize and a rule that is simply handled for you. In the same way you do not reimplement card-network compliance every time you take a payment, you should not have to reimplement content-transparency marking every time you generate an image.

On-Model handles at the...