The useful thing to understand about Google AI Image Labelling is what it cannot do.
It cannot detect an AI-generated image (currently). There is no analysis happening, no forensic examination of pixels. What exists is a system for carrying information that was attached when the image was created, and reading it later if it survived the journey.
Google AI image labelling explained accurately is therefore a story about metadata and its fragility rather than about detection.
Google AI Image Labelling Explained: Limits and Use
How it actually works
Some tools and cameras attach provenance information to an image when it is made: what created it, when, and whether it was edited afterwards. The C2PA standard, published as Content Credentials, is the common format for this.
Platforms that support the standard can read that information and surface it, which is what appears in features like Google’s “About this image”.
The chain depends entirely on the metadata surviving. If it is present and intact, context can be shown. If it is absent, nothing can be inferred, and the absence means nothing in particular.
Why the image metadata frequently disappears
This is the practical limitation and it is substantial.
Image metadata is stripped by many ordinary operations. Screenshotting removes it. Re-encoding removes it. Uploading to platforms that reprocess images frequently removes it. Editing in software that does not support the standard removes it.
So an image with no credentials might be a photograph, a generated image whose metadata was stripped, or anything in between. Absence of a label is not evidence of anything.
Nor does presence prove much beyond what the creating tool asserted. Credentials record a claim made at creation, not an independent verification of it.
What this means for a business publishing AI images
Less than the coverage suggests, and it points in a useful direction anyway.
- Disclose when it matters. If a generated image could reasonably be mistaken for a photograph of your actual work, premises or people, say so. This is a straightforward accuracy question rather than a technical one, and misleading imagery is misleading whether or not any platform detects it.
- Do not rely on labels to do the disclosing. The metadata may not survive, and most readers will never look for it. If the disclosure matters, put it where a person will see it.
- Prefer real photography of your own work. Beyond avoiding the question entirely, it is more specific and more convincing than anything generated, particularly for a local business where recognisability is part of the point.
The claims angle
Imagery makes claims. A photograph implying your team, your premises or your completed work asserts something about your business, and generated imagery presenting itself as documentary is a misleading claim regardless of the technology used.
That principle applies under ordinary consumer protection expectations rather than any specific AI rule, and it is worth applying before regulation catches up with the technology.
What Google AI image labelling explained sensibly means for your workflow
The temptation after reading about any provenance system is to build a process around it. That is premature.
Support varies between tools, platforms and editing software, and the standard is still settling. A workflow depending on credentials surviving end to end will break somewhere you did not test, quietly, without anybody noticing until somebody asks.
The durable position is to decide your disclosure practice independently of the technology. If an image would mislead somebody about your work, say so in words on the page. That holds whether or not any platform reads the metadata, and it does not need updating when the standard changes.
Practical handling
Keep provenance information intact where your workflow allows, since stripping it removes context others might find useful.
Write alternative text describing what an image conveys in context, which serves accessibility and is unrelated to how the image was produced. Work towards WCAG 2.1 Level AA.
And keep images efficiently delivered. Where performance and environmental impact both apply, treat them as the same lever: reducing unnecessary data transfer and processing supports a faster experience while lowering estimated digital carbon, though environmental figures remain modelled rather than directly measured.
Our search engine optimisation work covers image handling as a technical, accessibility and credibility question together.
