How to Spot an AI-Generated Image

AI image generators have become astonishingly capable, producing pictures that can be nearly indistinguishable from photographs. As these images spread across the internet, in news, on social media, in advertising, being able to recognise them matters, for avoiding being misled, for judging what is real, and for engaging critically with what you see. There are still tells that can reveal an AI-generated image, and knowing them is useful. But the uncomfortable truth is that these tells are disappearing fast, and the visual giveaways that worked last year are mostly gone. This guide covers the current signs of AI-generated images, why detecting them by eye is becoming so much harder, and why the most reliable approach is shifting from examining pixels to questioning context and provenance.

The classic tells

AI image generators have long betrayed themselves in the details, and examining those details still catches many fakes. Hands are a notorious weak point, with too many or too few fingers, or fingers that bend strangely. Text in images, on signs, labels or clothing, often comes out as garbled, nonsensical characters. Backgrounds can dissolve into incoherent mush when you look closely. And small elements like teeth, jewellery, and patterns sometimes fail to add up on inspection. Zooming into these areas remains a practical first check.

These tells arise because generating coherent fine detail, especially in complex structures like hands or readable text, has been genuinely hard for image models. When something in an image seems slightly off, examining the hands, any text, the background, and small repeated details often reveals the telltale errors of AI generation. For now, this close inspection is a useful skill, catching the many AI images that still contain these characteristic flaws. The important caveat, developed below, is that these classic tells are exactly the flaws that each new generation of models is fixing, so their reliability is fading even as they remain useful today.

The subtler signs

Beyond obvious glitches, AI-generated images often carry subtler qualities that can hint at their origin. Many have an over-smooth, almost too-perfect look, a flawlessness that feels slightly unnatural compared to the imperfect texture of real photographs. Lighting and reflections can be subtly inconsistent, not matching in ways that are hard to articulate but register as off. Faces in particular can sit in an uncanny valley, technically perfect yet somehow not quite right, feeling artificial without an obvious specific flaw.

These subtler signs require a more practised eye than the obvious glitches, but they can flag AI images that have overcome the classic tells. The sense that an image is too clean, too perfect, or subtly inconsistent in its lighting and reflections is worth attending to. That said, these cues are even less reliable than the classic tells, since real photos can be smooth and AI images can incorporate deliberate imperfection. They are hints to prompt closer scrutiny rather than definitive proof. As models improve, even these subtle qualities are becoming harder to rely on, which points toward the deeper problem with visual detection.

It is getting genuinely harder

The uncomfortable reality is that the tells for spotting AI images are disappearing fast, and this trend is central to understanding the situation. Each new generation of image models specifically fixes the flaws of the last: hands are getting right, text is becoming legible, backgrounds stay coherent, and the subtle unnatural qualities are diminishing. The reliable visual giveaways of even a year ago are largely gone in the latest models, and the trajectory points toward images that the eye simply cannot distinguish from real ones.

This means that visual detection, examining the image itself for flaws, is a diminishing strategy. It still works against images from older or weaker models, but against the cutting edge it increasingly fails. Relying on your ability to spot the tells is therefore becoming less and less dependable, and will likely become unreliable altogether as the technology advances. This is not a reason to stop looking, the tells still catch many images today, but it is a reason to understand that pixel-level detection is a shrinking capability, and to shift toward approaches that do not depend on the image containing detectable flaws.

Look at context, not just pixels

Because the images themselves are becoming unbeatable, the more durable questions are about context and provenance rather than pixels. Where did this image come from? Who posted or published it? Does a trusted, credible source confirm it? Does it depict something implausible or conveniently aligned with someone agenda? These questions about the image origin and context are becoming more reliable than visual inspection, because they do not depend on the image containing detectable flaws, which it increasingly does not.

This shift toward provenance is the key adaptation to a world of undetectable AI images. Rather than asking whether an image looks fake, which is becoming impossible to judge, ask whether it comes from a trustworthy source and holds up in context. An image from a reputable outlet, corroborated by other credible sources, is more trustworthy than a striking image of unknown origin circulating on social media, regardless of how the pixels look. Cultivating this habit, treating provenance and context as the primary test of an image credibility, is the sustainable approach as visual detection fades. The question becomes less can I see that this is fake and more can I trust where this came from.

Staying grounded as images get harder to trust

The broader lesson of increasingly perfect AI images is that we can no longer take at face value that a photograph shows something real, which is a genuine shift in how we relate to images. This calls for a healthy, general scepticism toward striking or consequential images, especially those of uncertain origin encountered online. Rather than assuming an image is real until proven otherwise, the wiser default is to withhold full trust until you have reason, through source and context, to grant it.

This does not mean disbelieving everything, which would be its own kind of failure, but developing a calibrated caution appropriate to a world where convincing fake images are easy to make. Combine the still-useful visual tells, while they last, with the more durable practice of questioning provenance and context, and maintain a general awareness that images can be fabricated convincingly. This grounded, sceptical-but-not-cynical stance is the realistic way to navigate a visual landscape where seeing is no longer straightforwardly believing. As the technology continues to advance, this shift from trusting images by default to verifying them by context becomes an essential everyday media skill.

Frequently asked questions

What are the signs an image is AI-generated?

Look closely at hands (extra or malformed fingers), any text (often garbled), backgrounds (can dissolve into nonsense), and small details like teeth and jewellery. Subtler signs include an over-smooth, too-perfect quality and inconsistent lighting or reflections. These tells still catch many images, but they are disappearing as models improve.

Can you always tell if an image was made by AI?

Increasingly, no. The visual tells are disappearing fast as each generation of image models fixes the last flaws, and the latest images can be indistinguishable from photographs. This is why the more reliable approach is shifting from examining the pixels to questioning provenance and context, where the image came from and whether a trusted source confirms it.