AI-Generated Damage Photos

Start with the checker above: drop in the photo the customer sent, ideally the original file rather than a screenshot. Treat the answer as one signal. Then ask for a second angle or a short video, compare the photo with the order, and decide on the whole case. Never reject a claim on a score alone.

The checker reads the file's Content Credentials and metadata first, in your browser, and sends the photo to a detection model only when those can't settle it. You get Likely AI-generated, Likely a real photo, Can't tell or Edited with AI, with a range and the reasons. It is the same tool our home page uses to detect AI images, with next steps set up for orders and refunds. AI-generated damage photos are a new problem, so the rest of this page covers what sellers report, where detection is weak, and a workflow that doesn't punish honest customers.

Sellers and fraud-prevention companies describe the same pattern: a customer receives an intact item, uses an AI tool to make a photo of it look damaged, and claims a refund. It works best where refunds are paid without a return, for cheap, fragile or perishable goods.

• In China, WIRED found at least a dozen posts from sellers and support staff on RedNote about suspected AI refund photos, including a bed sheet whose shipping label had gibberish characters and a ceramic mug with cracks that looked like torn paper. Bruce Schneier picked up the story on his blog in December 2025.
• Forter, a fraud-detection company, told WIRED that AI-doctored images in refund claims had grown by more than 15 percent since the start of that year.
• Ravelin, another fraud company, says 65% of the 6,200 shoppers in its survey agreed that AI has made false refund claims easier.

The merchants in WIRED's report named fresh groceries, low-cost beauty products and fragile items like ceramic cups as the most abused, partly because sellers often refund those without asking for the item back.

Most of the fakes Forter and Ravelin describe start from a real photo of the real item, with a crack, dent, stain or tear added by an AI tool. That is the hardest case for any detector, because most of the pixels really came from a camera, and it is the case our test didn't cover.

Sightengine's documentation describes its model as telling fully AI-generated images from real ones, and says it targets AI-edited images as well. We didn't test any edits or any damage photos, so we can't tell you how often a small AI-added crack gets caught. If the file's own record says it was edited with AI, the checker answers Edited with AI; a file edited with a tool that writes no record gives no such hint. A Likely a real photo answer on AI-generated damage photos therefore rules out less than it seems.

This workflow for suspected AI-generated damage photos adds a little friction to doubtful claims without punishing honest customers. It follows what fraud teams such as Ravelin recommend: low-friction requests first, harder ones only when something doesn't add up.

1. Run the original file through the checker. Ask for the photo as a file, not a screenshot, so detail and any credentials survive. Our test covered whole images, not damage photos or partial edits, so the answer is one signal, not proof.
2. Read the reasons, not just the label. A model score near the line means less than a signed credential.
3. Ask for a second angle and a short video. Ravelin says fake damage that stays consistent across several angles is much harder to produce, and a short pan around the item harder still. Forter warns that AI tools can already keep a defect in the same place across angles, so treat this as friction, not proof.
4. Compare with the order. Check the item, color, size, packaging and shipping label against what you sent. Text and labels are a weak spot for many generators.
5. Decide on the whole case. Look at the customer's history and the value of the item, and escalate an unclear claim rather than rejecting it.

The table above sums up what each request adds and where it falls short.

A false alarm on a damage claim lands on a customer who really did receive a broken item. In our test, neither main engine flagged any of the 50 real photos as AI, including 6 product photos, and the checker called 49 of them real and answered Can't tell for 1.

That is encouraging, not a guarantee. With 0 false alarms in 50 photos, the 95% confidence interval still runs up to 7.1%, and none of our real photos showed damage. Strict policies have a cost too: in a survey Forter cites, 16% of consumers said they had stopped shopping with a brand that made its return policy stricter. Ask for more evidence before you accuse anyone of sending AI-generated damage photos.

Our test measured how detectors handle whole images: 50 real photos and 50 AI images. It included no damage photos and no partial AI edits, so this page gives you a workflow for AI-generated damage photos, not a measured success rate for catching fake refund claims.

If you handle claims at volume, 3 free checks a day won't go far, and there is no bulk upload. Our notes on choosing a detection API cover the engines we tested and what they cost. For pictures of people and listings, see the AI photo detector; for any other picture, the AI picture detector; and for the full method, the four-step check.

No. It gives a likely answer with a range and reasons. A Likely AI-generated result is a reason to ask for more evidence, and a Likely a real photo result doesn't clear a photo with a small AI edit.

Yes, when you can. Screenshots and photos passed through chat apps lose detail and metadata, which weakens every check. Ask the customer to attach the photo itself.

The checker reads credentials and metadata in your browser. If a photo needs a model check, our server passes it once to a detection service (Sightengine, or Hive as a backup) and doesn't save it; we keep only a hash and the verdict. Check your own privacy policy too, and see ours.

Sometimes, if the AI tool signs its output and the file wasn't re-saved since. Often they don't, so a missing credential tells you nothing.