How to spot fake damage photos (and why spotting isn't enough)

A customer says the item arrived broken and sends a photo. It looks plausible. Maybe a little too plausible. Here are the checks worth a few minutes, what each can and can’t tell you, and why it’s more reliable to change how the photo gets taken.

Why this is harder in 2026

Faking damage used to take Photoshop skills. Now it takes a prompt. Practical Ecommerce (July 2026) notes that generative AI can fabricate damage photos, shipping records and complaint text, and that merchants usually approve refunds from a photo without ever inspecting the item. It cites NRF and Happy Returns figures of about $849.9 billion in US returns in 2025, with roughly 9% fraudulent. That figure covers all return fraud, not just AI photos.

It isn’t hypothetical. PYMNTS reported in March 2026 that Boll & Branch’s CEO spotted a “ripped sheets” photo where the tear “did not look like anything cotton does when it frays”. One image carried an AI watermark, and he then found several more tickets with damage photos that looked machine-generated.

The six common kinds of fake damage photo

AI-edited real photo. The customer photographs the item they actually received and asks an editor to add a crack, stain or tear. Everything but the damage is genuine, so it’s the hardest to catch.
Fully generated image. No real photo at all. Often shows a generic version of your product with details slightly off.
Reused photo. Taken from another claim, a review, a forum post or a different seller’s listing.
Photo of a screen or a print. Someone photographs an image displayed on a monitor or printed out, so the file looks like a fresh camera shot.
Old photo. Real damage, but from a previous item or from before your parcel arrived.
Stock or listing photo. Lifted from a stock library or a product page, sometimes with damage edited in.

Visual tells worth checking

Zoom in on the original file. Look for:

Lighting and shadows that disagree. The damaged area is lit from a different direction, or a chip casts no shadow when everything around it does.
Repeating texture. Wood grain, fabric weave or carpet that tiles in an obvious pattern, especially around the damage.
Warped text, labels and logos. Brand names, barcodes and labels are still a weak spot for generators. Compare stitching, ports and packaging against what you shipped.
Damage that ignores the material. Glass that cracks like paper, fabric that tears like plastic, a dent in something rigid with no stress marks around it. The Boll & Branch case above is exactly this: a tear that didn’t look like cotton.
Too-clean edges. A crack with perfectly smooth sides, a stain with a crisp outline, damage that stops neatly at an edge.

The honest caveat: modern editors remove many of these tells, especially when they edit a real photo rather than generate one from scratch. In the FraudBench study described below, human reviewers reached a balanced accuracy of only about 69% at telling fake damage from real damage. A clean visual check means “nothing obvious”, not “genuine”.

Metadata (EXIF) checks and their limits

Photos from a phone camera usually carry EXIF data: capture date, device model and sometimes location. You can view it in your computer’s file properties or with a free tool like ExifTool.

What to look for:

A capture date before the delivery date.
A device or software field that names an editing app or AI tool.
No camera data at all on a file the customer says came straight from their phone.

Limits you need to know:

Missing metadata is normal. Messaging apps strip it. Amnesty’s Citizen Evidence Lab notes that sharing an image the normal way on WhatsApp “will strip out all metadata”, and most social platforms do the same.
Metadata is easy to change. ExifTool itself can delete metadata and shift date/time values (feature list). Anyone can do the same.

So metadata can raise a flag (a date before delivery), but it can’t clear a photo.

Reverse image search

This catches reused, stock and listing photos. On a computer, go to google.com, click Search by image, and upload the file or drag it into the search box. In Chrome you can right-click an image and choose Search with Google Lens (Google help). Try your own listing photos too, since a customer may have edited your product shot.

A match is strong evidence. No match tells you little: AI-edited photos of a real item are new images and won’t show up anywhere.

Content Credentials (C2PA)

C2PA is an open standard for attaching a signed record of where an image came from and how it was edited, branded as Content Credentials (“a nutrition label for digital content”). You can inspect a file at contentcredentials.org/verify.

Where it stands at the time of writing (October 2026):

Google says the Pixel 10 is the first phone line to attach Content Credentials to every photo taken with Pixel Camera.
Samsung’s Galaxy S25 was reported in 2025 to add credentials only to AI-edited images, not ordinary photos.
The Content Authenticity Initiative says credentials “aren’t intended to prescriptively indicate whether a piece of content is ‘real’” (FAQ). They record history; they don’t judge truth.

Most customer photos will arrive without credentials. If a file carries credentials showing an AI edit, that’s useful. If it carries none, that proves nothing.

How well do AI detectors work?

Not well enough to decide a refund on their own. The FraudBench paper (first posted May 2026, revised September 2026) built a benchmark from real e-commerce, food delivery and travel review photos, then created fake-damage versions of genuinely undamaged items using 12 image editing and generation models. Its findings, from version 2 of the paper:

General multimodal AI models usually recognized real damage, but their detection rate on fakes was “far below the 50% baseline on most generator subsets”. Averaged across fake subsets, they caught only about 23%.
Specialized AI-image detectors did better overall but were “inconsistent across generators” and some flagged real damage as fake. One flagged about 95% of genuine damage photos.
The authors conclude there is “a clear gap between generic AI image detection and reliable claim-conditioned refund-evidence verification.”

This is an arms race. Detectors learn today’s generators’ quirks; new generators arrive without them. A June 2026 study of Chinese merchants and platform workers found the same pattern in the field: AI screening helps, but it is limited by how good the generators have become, so merchants also ask for things that are harder to fake, like multi-angle video.

The alternative: control how the photo is taken

Every check above judges a photo the customer chose to send. It’s more reliable to set the conditions before the photo exists:

A fresh photo with a one-time code. Ask the customer to write a code you give them (and today’s date) on paper and keep it in shot. A photo prepared earlier can’t contain a code that didn’t exist yet. Editing one in is possible, but it’s another thing to get right.
A live camera, not an upload. If the photo has to be taken in the moment rather than picked from the camera roll, old, reused and pre-edited images are out.
Multiple angles. Whole item, close-up of the damage, and the packaging with the shipping label. Faking three consistent views of the same item is much harder than faking one.
A short video turning the item around, if your channel supports it.

Keep the request polite and routine. Most damage claims are genuine. For what to do once you suspect an AI photo, see the US guide or the UK guide.

Printable checklist

Before you decide on a suspicious damage photo:

Worked example

Say you sell ceramic mugs at $38 each. A customer messages two days after delivery: “Arrived with a big crack, see photo.” You run the checklist:

Visual: the crack is sharp and dark, but the glaze around it shows no chipping, and the light on the crack comes from the left while the mug’s highlight is on the right. Suspicious, not conclusive.
Metadata: none. The photo came through a messaging app, so that’s expected.
Reverse search: no matches. Also expected if it’s an edit of the real mug.
Credentials: none.

You have doubts but no proof. So you reply: “Sorry about that. To process this quickly, could you send three new photos: the whole mug, a close-up of the crack, and the box with the shipping label? Please write M-4821 and today’s date on a piece of paper and keep it in each shot.”

If the new photos show a genuine crack with the code in shot, you refund or replace and move on. If the customer won’t send them, you have a calm, documented record of a reasonable request, which matters if the case turns into a chargeback (see chargeback evidence for “item arrived damaged”). Check your marketplace’s and your local consumer law’s rules before refusing a refund outright.

How ClaimCam handles this

ClaimCam turns the “fresh photo with a code” request into a link you paste into your email or helpdesk reply (marketplaces like Amazon and eBay don’t allow links in buyer messages). The customer’s phone opens a live camera in the browser with no upload button, and they keep a one-time code (valid 10 minutes) on paper in shot for three guided photos: whole item, close-up, packaging. The server fingerprints and timestamps every frame, and an AI check reads the handwritten code, flags photos that look like a screen or a print, and checks it’s the same item throughout. You get a private evidence page you can save as a PDF for a dispute. It’s deterrence plus evidence, not proof: a customer who really damaged the item can still photograph real damage. claimcam.link