A couple of years ago you could spot an AI image in a second. Too many fingers, melted text on a sign, jewellery that fused into the skin. Those days are mostly gone. The generators got good, and the easy tells faded with them, which is a problem when knowing whether an image is real now matters for news, scams, dating profiles, and product photos that turn out to be of products that do not exist.
So the question is worth taking seriously, and the honest answer has layers. There is what your eyes can still catch, there is a genuinely reliable technical method that most people have never heard of, and there is the context around the image. None of them is perfect on its own, but together they get you a long way. Let us start with the one you already have, then build up to the stronger ones.
What your eyes can still catch
Visual tells are not dead, they have just moved. Hands are better now but still go wrong in busy scenes. The reliable giveaways these days are subtler, so it pays to know where to look. Backgrounds are a good place to start, because generators spend their effort on the subject and get lazy behind it. Look for warped door frames, text on signs that turns to gibberish when you zoom in, patterns like tiles or bricks that do not line up, and reflections that do not match what should be reflected.
There is also a certain quality to a lot of AI images that is hard to name until you have seen enough of them: skin and surfaces look slightly too smooth and even, the lighting is a touch too perfect, and everything has the same softness as if it were lit in the same flawless studio. That instinct is real and useful. The trouble is that it is an instinct, not proof, and it is exactly the thing that improves with every new model. So treat your eyes as an early warning, not a verdict, and lean on the next method when it actually matters.
The reliable method almost nobody uses: Content Credentials
Here is the part that changes the game, and it is hiding inside the image file itself. A standard called C2PA, branded as Content Credentials, attaches a tamper-evident record of where an image came from and what was done to it. Think of it as a cryptographically signed label baked into the file. When it is present, it is the closest thing we have to proof.
What makes this powerful is that it is not a guess. The record is signed, so it cannot be quietly forged, and as of 2026 the major sources embed it by default: Adobe Firefly, OpenAI's tools, Google's image models, and even the cameras in recent phones from Samsung, Google, and Apple. You can check for it yourself. Our image metadata viewer reads what is stored in a file, including the provenance information, right in your browser. If the credential is there and says an image was AI-generated, you have your answer.
The catch you have to understand
I have to be straight about the limit, because it is the part people get wrong. The absence of Content Credentials does not mean an image is real. Most images on the internet today carry no credential at all, for ordinary reasons: screenshots strip it, most social platforms remove it when they recompress your upload, and plenty of AI tools, especially open-source ones, never add it in the first place. So a "missing" result tells you nothing on its own.
That is why the credential is a one-way signal. Present and signed, it is strong evidence. Absent, it just sends you back to the other methods. Which is a good moment to add the last one.
Reverse search and plain context
When the file will not tell you, the wider internet often will. Run the image through a reverse image search and see where else it appears. A real news photo usually shows up on multiple reputable outlets with consistent dates and captions. An AI image, or a stolen and re-captioned one, tends to have no sensible history, or a history that contradicts the story it is being used to tell. Context does a lot of the work that pixels cannot.
Ask the obvious questions too. Who posted it, and do they have a reason to mislead you? Does the claim around the image sound designed to make you angry or to rush you? A surprising share of fake images are exposed not by forensic detail but by a caption that falls apart the moment you check a date or a location.
Putting it together
No single test is foolproof, so the practical approach is to stack them in order of strength. Glance for the visual tells, because they are free and instant. Check the file for Content Credentials, because when they exist they are the strongest evidence you can get. And run a reverse search to see whether the image has a believable history. Any one of these can be fooled. All three pointing the same way rarely are.
The honest takeaway is that "spot the AI by eye" is no longer a skill you can rely on, and pretending otherwise is how people get fooled. The reliable signal now lives in the file and in the context, not just in the pixels, so that is where to look when it actually counts.
