Most owners decide whether a complaint photo is genuine in about two seconds, on gut feel. Gut feel is not bad — you have seen a lot of your own food — but it is exactly what a doctored photo is designed to beat. Here is what to look at instead, from the quick checks anyone can do to the ones that need a tool.

First, get the original file

Everything below works better on the original image than on a screenshot. A screenshot re-encodes the picture and strips the camera metadata; a photo forwarded through WhatsApp is recompressed and stripped too. If the complaint arrived through a delivery app's dispute form, download the attachment. If it came by email, save the attachment rather than screenshotting the email. If it came by WhatsApp, you may be out of luck on metadata, but the visual checks still apply.

Seven checks you can do on your phone

1. Is the light doing one thing? In a real photo the light comes from somewhere — a window, a ceiling lamp, the phone's flash — and everything on the table agrees about it. Shadows point the same way, the same side of every object is bright. A defect that is lit differently from the food around it was often not there when the shutter fired.

2. Does the "bad" area look sharper or blurrier than its surroundings? Pasting or painting something in almost always changes its texture. Pinch-zoom on the hair, the mould spot or the burnt patch and compare the grain with the food a centimetre away. A crisp object on a soft photo, or a smeary patch on a crisp photo, is a flag.

3. Do the edges make sense? Look at where the defect meets the food. Real objects sit in the food — a hair goes under a noodle, a piece of plastic casts a tiny shadow. Added ones sit on it, with a hard outline or a faint halo.

4. Is the whole photo unnaturally dark, green or grey? The most common edit is not adding anything, it is dragging exposure and warmth down until fresh food reads as spoiled. Look at things that are not food in the frame — the box, a napkin, a hand. If white cardboard is grey-green, the colour of the food is not telling you anything.

5. Is this your box? Check the packaging, the sticker, the receipt, the cutlery. Fraudsters reuse photos. A container you do not use, a label with a different font, or a portion size that does not match the order is the fastest possible tell.

6. Reverse-search it. Google Lens or TinEye will find a photo that has been posted anywhere public before. It takes ten seconds and catches the laziest version of the scam — the one where the photo is not of your food at all.

7. Look at the things AI gets wrong. Generated food photos are now very good at food and still poor at everything around it: text on packaging turns to gibberish, fingers are the wrong number, the pattern on a plate repeats too perfectly, reflections do not match. If the plate is real and the box looks like a dream, be suspicious.

What the metadata tells you — and what it does not

A photo straight off a phone carries EXIF data: the phone model, the time it was taken, sometimes the location, and often a note of the software that last saved it. Three things are useful:

  • A software tag from an editor (Photoshop, Snapseed, a phone's own editor) means the file was saved by something other than the camera. That is not proof of tampering — people crop — but it is a reason to look harder.
  • A capture time that is hours before the order was placed, or days before, means the photo is not of this order.
  • No metadata at all on a file that supposedly came straight from a phone is itself informative: something removed it, whether a messaging app or a person.

What metadata cannot do is clear a photo. A genuine, untouched photo of food that a customer deliberately dropped on the floor has perfect metadata. Metadata catches carelessness, not intent.

What a forensics tool adds

The checks above catch the crude versions. The good versions — a defect inpainted by a modern AI tool onto a real photo of your food, with the file saved through a camera app to keep the metadata clean — look right to the eye. That is where pixel-level analysis earns its keep.

Tools like FraudBite look at things a person cannot: whether the noise pattern in one region matches the rest of the sensor's noise, whether the compression history of the "defect" is the same as the food around it, whether the image carries the statistical fingerprints of a generative model. The output is a risk score and a heatmap — the regions that do not belong, marked on the photo — plus a plain-language list of what was found.

Two honest caveats. First, the score is a probability, not a verdict; read our FAQ answer on what a high score means. Second, heavy recompression (a photo forwarded three times through messaging apps) degrades what any tool can see, which is another reason to insist on the original file.

The two things that never prove anything alone

The food looks bad. Food photographed on a phone under a kitchen light at 10 p.m. looks bad. That is not evidence of anything.

The customer has complained before. Regulars complain; so do serial abusers. A history is a reason to check carefully, not a conclusion. Keep the decision on the photo and the order, and keep the tone on the evidence.

When you have a photo in front of you and want a second opinion in seconds, run one of our sample photos through the check to see what the output looks like — then decide whether it belongs in your Friday-night routine.