How to Spot AI-Generated Images: What Still Works in 2026

The hand-counting trick is finished. Current image models get fingers right most of the time, and the advice everyone learned in 2023 now mostly produces false confidence. The tells that remain are subtler, they differ depending on what kind of image you are looking at, and the single most reliable method does not involve examining the picture at all.

Why do AI images look weird when the hands are right?

Image models do not draw objects. They generate a field of pixels that is statistically consistent with the description, which means every region can look correct while the relationships between regions quietly fail. That is the origin of nearly every AI image artifact worth knowing about: not a bad hand, but a boundary, a reflection or a repetition where two locally-plausible regions do not agree with each other.

Hands were an easy example of that failure, which is why they became the famous one. They were fixed by training, not by solving the underlying problem. The checks below target the parts that have not been fixed.

1. Read the small text

Text is still the most consistent weakness. Models generate letterforms that look like writing rather than reproducing actual words, so signage, book spines, product labels and street signs come out as convincing-looking nonsense.

Zoom in on any text in the background. Real photographs have readable, correctly spelled words. Generated images frequently have letters that almost form words, or spelling that drifts halfway through. Large headline text is often handled correctly now; small background text much less so.

2. Check where objects meet

Look at boundaries rather than surfaces. Specifically:

Where hair meets a background, particularly against a busy scene
Where glasses frames cross the face, and whether the arms line up on both sides
Where jewellery, watch straps or necklaces pass behind something
Where a hand grips an object, and whether the object continues correctly on the other side

Models render each region plausibly but do not always maintain continuity across a boundary. A chain that goes behind a collar and comes out at a slightly wrong angle is a common signature.

3. Look for repetition in crowds and patterns

Scan the background of any group scene. Generated crowds tend to contain repeated faces, identical clothing on people standing near each other, or a pattern in fabric or brickwork that repeats too regularly then abruptly stops matching.

Real photographs have messy, irregular variation. Generated ones often have variation that looks correct at a glance and reveals itself as patterned when you look for a few seconds longer.

4. Test the physics: reflections, shadows and support

Reflections are difficult. Check whether what appears in a mirror, a window or a puddle actually corresponds to what is in the scene. Check whether shadows all fall in a direction consistent with a single light source.

Also worth checking: whether a person is plausibly supported by what they are standing or sitting on, and whether the perspective of a floor or table stays consistent from one side of the frame to the other.

5. Notice the too-perfect finish

Harder to describe, but real once you notice it. Generated images often have an unusually even quality — everything equally sharp, lighting uniformly flattering, no dust, no sensor noise, no slightly blown highlight, no awkward crop.

Real photographs are compromised. Something is usually slightly out of focus, slightly over-exposed, or framed a little wrong. Perfection across the entire frame is itself a signal.

6. Reverse image search beats looking at the picture

This is the one that actually works, and it does not require you to be good at spotting artefacts.

Run a reverse image search. Google's "About this image" will tell you when Google first saw a similar version, which pages carried it earliest, and what other sources say about it. TinEye indexes differently and is worth a second look when Google finds nothing.

What you are looking for is history. A real news photograph appears on multiple outlets, with dates, with a photographer credited. An image that appears nowhere before yesterday, or only on accounts that share it without attribution, is worth doubting regardless of how it looks.

For anything consequential — a claimed news event, a disaster, something politically charged — this beats visual inspection every time. The question is not "does this look generated" but "where did this come from and who else has it".

How to spot AI-generated travel photos

Travel imagery is where generated pictures do the most quiet damage, because the whole point of a destination photo is that you have never been there and cannot tell what it should look like.

Signs of AI-generated travel images

Geography that does not assemble. Two real landmarks rendered in the same frame at the wrong distance or bearing from each other. If a skyline looks right but the buildings sit in an order you cannot verify, check it against satellite or street-level imagery in any maps app.
Signage in the wrong script. Foreign-language signs are small background text, which is exactly what models are worst at. A sign in Thai, Greek or Japanese that resolves into near-characters is a strong tell.
Impossible light. Golden-hour lighting on every surface, including surfaces facing away from the sun. Real landscape photography has shadowed sides.
Water that does not behave. Reflections in a lake or canal that do not invert the scene above them, or a horizon that shifts height across the frame.
Crowds at famous sites. Repeated faces and duplicated clothing in the middle distance, which is where models stop rendering individuals carefully.

AI-generated landscape photos are the easiest subcategory to fake convincingly, because there is no text, no hands and no crowd — so lean on provenance for these rather than on your eyes.

How to spot fake hotel pictures

Hotel and rental listings have a specific pattern, because the incentive is to show a room that does not exist:

A window view that does not match the light in the room, or that shows a landmark the property is nowhere near.
Furniture proportions that are subtly wrong — a bed too wide for its headboard, a bath that does not fit the wall behind it.
Doorways and skirting boards that change height as they cross the frame.
Every photo shot at the same impossible wide angle with no distortion.

The check that settles it: compare the listing's photos against traveller-uploaded photos of the same property. Guest photos are badly lit, badly framed and real. If a property has fifty polished images and no guest pictures that resemble them, that is your answer.

AI photos in listings: Marketplace, property and product shots

Facebook Marketplace and second-hand listings

Generated photos on Marketplace usually give themselves away by what is missing rather than what is wrong. Genuine second-hand photos have a room behind them, uneven light, and the photographer's reflection in anything shiny. AI photos of second-hand goods tend to show a single angle, no wear, no context and no reflection.

The decisive move costs nothing: ask the seller for a new photo of the item next to something specific — today's date on a piece of paper, or a particular object you name. Generation cannot satisfy an arbitrary, unpredictable request on demand. A seller who will not do it has told you what you needed to know.

Listings that use generated photos frequently also run credential phishing on the side, steering you to a lookalike payment page. A password manager is a useful defence here for a reason that has nothing to do with password strength: it matches the domain before it fills anything, so it silently refuses to autofill on a copycat site that has fooled you.

AI-generated real estate photos

Property listings are the highest-stakes version of the same problem. Check stairs, which models render badly — tread depth that varies, a banister that changes side. Check that ceiling fixtures are not duplicated across rooms that share a wall. Check whether the windows in different photos of the same house agree about the time of day and the weather.

Then check the listing against street-level imagery. A house exterior is a matter of public record in a way an interior is not.

AI-generated product photos on Amazon and other stores

Product photography is now routinely AI-assisted, which is not itself fraud. What matters is whether the product in the photo is the product being sold. Look at packaging text — this is check 1 again, and it is where generated product shots fail most reliably. Then compare the manufacturer's own images with the seller's, and read the review photos, which are the only images in a listing that a seller does not control.

AI profile pictures on dating apps, LinkedIn and Instagram

Face generation is the most mature capability of all, so the visual checks are weakest here and behavioural checks matter more.

Count the photos. Generated identities are photo-poor. One flawless picture, or several that are all the same crop, angle and lighting, is the pattern.
Look at the ears and the eyes. Asymmetric or mismatched earrings, and eyes that sit at exactly the same height and spacing across multiple images, both suggest a generated face.
Check the background blur. Real lens blur increases with distance and has a characteristic shape. Generated blur is often uniform across the whole background regardless of depth.
For a LinkedIn profile photo, cross-check the person against the company they claim. A real professional leaves a trail — a conference listing, a co-authored post, a colleague who has interacted with them.
For dating profiles, reverse image search every photo before any conversation moves to a different platform. That single step defeats most of it.

Working out whether an Instagram photo is AI is harder, because heavy editing and generation produce a similar too-smooth result. Treat the account, not the image, as the unit of analysis: posting history, comment patterns and whether the same face appears in other people's photos.

Do AI images have metadata? EXIF, C2PA and Content Credentials

Sometimes, and it is worth checking, but the absence of metadata proves nothing.

Every photo from a real camera or phone carries EXIF data: device model, lens, exposure, often GPS coordinates. Generated images either carry no EXIF or carry fields that do not correspond to any real device. You can check image metadata by viewing file properties on your computer, or with any EXIF viewer.

Some generators now embed provenance deliberately. The C2PA standard defines a signed, tamper-evident record of how an image was made and edited, and the Content Credentials Verify tool will read that record out of any image you give it. When a credential is present it is strong evidence. That is the useful direction of the test.

The problem is the other direction. Almost every social platform strips metadata on upload, and anyone can remove it deliberately in seconds — re-saving an image through almost any editor discards it, including our own browser-based image compressor. So a missing credential is entirely normal for an ordinary photo that has been through Instagram, and tells you close to nothing. Google's own documentation makes the same point about the metadata it surfaces: it can be modified by generation and editing tools, so it may not be accurate.

This cuts both ways for your own photos, incidentally: the EXIF data in pictures you share can include exactly where you took them. That is one of the settings worth reviewing in what to turn off on your phone.

Are AI image detectors accurate?

Not reliably enough to settle anything on their own.

Detection tools fail in both directions — flagging real photographs as generated and clearing generated images as real. The failure is worst in exactly the conditions that matter, because detectors are trained on clean source files and the images you actually encounter have been screenshotted, recompressed and resized by a social platform first. Each of those steps destroys the statistical traces detectors look for.

The Reuters Institute's review of detection tools during the 2024 election cycle found the same pattern in professional use: how AI detection tools work and where they fail documents tools that handled well-known public figures but faltered on ordinary people, and a detector built for still images confidently misreading a video screenshot.

Treat any detector score as one weak input. A high confidence reading is a reason to look harder, never a conclusion.

Video is a different problem with a better first step. The major video generators embed provenance signals — invisible watermarks and file metadata — that still images usually lack, so there is something definitive to check before you read the picture at all. Do AI videos have a watermark? covers what Sora and Veo actually leave in a file, how to check it for free, and the visual tells to fall back on when nothing is there.

A workable habit

You do not need to analyse every image you see. Apply effort in proportion to consequence.

For something that would change your opinion, that you are about to share, or that is making a strong claim about a real event: spend thirty seconds on a reverse image search. For something you are about to pay for: ask for a new photo that could not have been generated in advance.

Those two habits catch more than all the visual checks combined, and they keep working as the models improve. The visual tells will keep degrading. Provenance does not.