Fake and AI-generated damage claims: the complete guide for online stores

Key points

• A fake damage claim asks for a refund or replacement for damage that did not happen, did not happen in transit, or happened to a different item. Generative image tools have made the evidence for one cheap to produce, and Signifyd reports "item not as described" claims up 49% in North America in early 2026, with AI-generated images of damaged goods named as a driver.¹
• Nobody has yet measured how many damage claims are false. The honest starting point is that most are genuine, which is why the process matters more than suspicion.
• Checking the photo after it arrives is a losing race. Metadata is easily stripped and AI-image detectors lose accuracy on real-world images, so a convincing file can pass and a genuine one can be doubted.²³
• The reliable answer is to change where the evidence comes from: capture the item live, tie it to the order, and judge it against data the customer does not control, such as the tracking number and your own product images.
• Whatever tools you use, decide on recorded evidence, keep what a carrier or card network would ask for, and give every customer the same process.

What is a fake damage claim?

Damage claims have always been the easiest kind of refund to ask for. The customer has the item and the store does not, so the store has to judge the claim from whatever the customer chooses to send. For years that meant a photo, and a photo was reasonable evidence because faking one took effort and skill.

That is no longer true. Image tools that add a crack, a dent or a stain to an ordinary photo are free, run on a phone and take seconds. Forter, which screens refunds for large retailers, describes fraudsters adding "a dent, tear, or mark anywhere on the image of the goods" and warns that the images "are now so good that you really can't tell."⁴ In its own reporting Forter calls AI-generated damage "the fastest-growing form of return abuse," a finding it attributes to its 2025 Cyber Month report.⁵

The cases that have surfaced show the range. In China, police found that a shopper had "used a mobile phone to create a synthetic video of dead crabs" to win a 195 yuan refund from a seafood seller, and detained the buyer for eight days.⁶ In the United States, the bedding brand Boll & Branch received photos of torn sheets that its chief executive said were "so blatantly AI that I couldn't believe it," and the drinkware brand Bogg received "the exact same photo the customer had sent before, only with damage added."⁷ Researchers who interviewed Chinese merchants and platform workers in 2026 concluded that generative AI lets attackers "fabricate hyper-realistic evidence," which breaks the old assumption that a photo is hard to fake.⁸

How common are fake damage claims?

The honest answer is that nobody knows yet, and anyone quoting a precise share of fake damage claims is guessing. Writing in July 2026, Practical Ecommerce noted that it had "seen no credible data on the extent of AI-assisted refund fraud in the United States."⁹ What does exist are wider measures of returns and claims, each from its own survey and each worth reading on its own terms.

The brands that have spoken publicly describe something real but not yet overwhelming. Bogg said AI-altered photos arrive only a couple of times a month, and Boll & Branch called its cases "very few" while adding that "there's probably some that got through that we didn't realize."⁷ That last remark is the important one. A false claim that succeeds leaves no trace, so the visible cases are the clumsy ones.

How are fake damage claims made?

False claims fall into a handful of patterns. Knowing them matters because each one defeats a different check, and a process that stops one can wave another straight through.

The last two rows are the reason photo forensics alone can never close the gap. A photo can be completely authentic and still be evidence of the wrong thing. The question that matters is not whether the file was edited but whether it shows this item, from this order, damaged in transit.

What are the signs a damage claim needs a closer look?

None of the signs below settles anything on its own, and honest claims show some of them all the time. Treat them as reasons to ask for better evidence, never as grounds to refuse.

In the photo

• Damage that looks painted on: a crack that ignores the material's grain or edges, a tear whose fibres do not separate, a stain with no depth or soak.
• Lighting or shadows on the damaged area that do not match the rest of the scene.
• Text, logos or labels on the item or packaging that are slightly warped, misspelled or blurred where the rest is sharp.
• A photo that matches your own product image a little too closely, as if it started from your listing.
• No packaging, no label and no context at all, just the item on a plain surface.

Around the claim

• The claim arrives long after delivery, or before the tracking shows the parcel delivered.
• The customer cannot show the shipping label or the box, or has already thrown them away.
• A claim on every order, or several accounts with the same address or payment details.
• Pressure to settle quickly by refund without a return, or a refusal to take any second step.
• Damage that does not fit how the item was packed: a crushed corner on an item that shipped in a rigid case.

The comparison of photo forensics and live capture explains why a check that starts and ends with the photo is the weakest place to defend.

Why does checking the photo keep failing?

The natural response to an AI-generated photo is to check it for AI. It is also the response most likely to fail, for three reasons that are getting stronger, not weaker.

The metadata is usually gone. Content Credentials, the industry standard for recording where an image came from, are cryptographically secure, but the Content Authenticity Initiative notes they are "easily stripped" and that "it is still common for social media and content platforms to remove metadata."² The C2PA standard itself acknowledges that "metadata can be removed inadvertently or intentionally."¹² A genuine customer photo that went through a messaging app looks exactly as anonymous as a forged one.

Detectors trail the generators. A 2025 study that tested eleven AI-image detectors found they "suffer significant performance drops on real-world data" despite high accuracy in controlled settings.³ A 2026 study tracking detectors over time found "a consistent decline in performance" as newer generators appeared.¹³ A detector is trained on yesterday's generators and judges today's images. When it is wrong in one direction a fake passes. When it is wrong in the other an honest customer is accused.

Authentic photos of the wrong thing pass every check. A reused picture of real damage, or a real photo of an item broken at home, contains nothing for forensics to find. Inspection after the fact can only ever answer whether a file was manipulated. It cannot say whether the file shows your item, damaged in transit.

The way out is to stop treating the photo as the evidence. Evidence is strongest when it is created under conditions the customer does not control and checked against facts the customer cannot change.

A fair process for every damage claim

The process below works whether you handle ten claims a month or a thousand. It gives every customer the same path, gets the evidence in one step instead of five emails, and leaves you with a record that stands up later.

1. Acknowledge without suspicion. Thank the customer, say you will sort it out, and explain the one thing you need from them. Never imply that you doubt them, even when you do.
2. Collect the evidence once, tied to the order. Ask for the item, the damage, the packaging and the shipping label in a single step, captured now rather than chosen from the camera roll. Every extra round of "could you send one more photo" costs the honest customer patience and teaches the dishonest one what to fake.
3. Check it against what you already know. The tracking number on the label should match the order. The item should match your own product photos. The timing should fit the delivery date. These facts come from your systems, not from the customer.
4. Decide on the evidence, with a rule your team shares. Strong evidence gets a fast refund or replacement. Weak or missing evidence gets a polite second step, not a refusal. Write the rule down so two agents give the same answer to the same claim.
5. Record the outcome and keep the evidence. Note what you decided and why. If the customer later disputes the charge, or you claim from the carrier, the record is what you will be asked for.

What evidence settles a damage claim?

A damage claim can end up in front of three different judges: your own team, the carrier and the customer's card issuer. Each asks for slightly different evidence, and the cheapest moment to collect all of it is the first one.

Shopify itself treats damage as its own category. Its return reasons include "Damaged or defective" alongside "Item not as described," and its chargeback reasons describe a product that was "defective, damaged, or not as described."¹⁸¹⁹ Recording the reason the customer chose, with the evidence, keeps those trails consistent.

How should your policy handle damage claims?

Policy is where most stores accidentally invite false claims. The common one is the refund without a return. Reuters reported in 2023 that 59% of retailers surveyed by goTRG offered "returnless" or "keep it" refunds on items that cost more to ship back than they were worth.²⁰ It makes sense for a cheap item, but it also makes the photo the only thing standing between a claim and a payout. In China, China Daily reported that "refund only" policies "have increasingly been exploited by dishonest customers seeking refunds without sending back merchandise."⁶

A good damage policy sets expectations without accusing anyone. It says how soon after delivery a claim should be made, asks the customer to keep the packaging until the claim is settled, and explains that claims are checked with a short guided capture. Honest customers read that as a store that takes care of problems. The few who were hoping for an easy refund read it differently.

• A clear window. Ask for damage to be reported within a set number of days of delivery, and shorter for perishables.
• Keep the packaging. It is what the carrier will ask for, and it is evidence for you.
• One evidence step for everyone. The same capture for every claim keeps it fair and keeps your team out of judgement calls about who looks trustworthy.
• Keep-it refunds by rule, not by default. Reserve them for low-value items with strong evidence, rather than for any claim with a photo.

Where this is heading

Image and video generators will keep getting better, cheaper and easier to use, and every improvement makes a submitted photo less meaningful as evidence. Provenance standards like Content Credentials will help in places where they are carried end to end, but a customer photo forwarded through a chat app is unlikely to be one of them for some time.

The stores that handle this well will be the ones that stopped asking whether a photo is real and started asking where the evidence came from. Evidence captured live, tied to the order and checked against data the customer does not control is evidence a generator cannot produce. Everything else is a contest between your attention and a tool that improves every month.

Questions

Sources

1. Signifyd, 2026 State of Fraud Report: Fraud Trends and AI-Powered Fraud Insights, 2026.
2. Content Authenticity Initiative, The three pillars of provenance that make up durable Content Credentials, January 22, 2026.
3. arXiv, Is Artificial Intelligence Generated Image Detection a Solved Problem? (Li et al.), May 2025, revised October 2025.
4. Forter, How Fake GenAI Images Are Fueling Refund Fraud, June 24, 2025.
5. Forter, What Is Return Abuse? Return Fraud's Costlier Cousin, April 15, 2026.
6. China Daily, Police punish shopper after AI video used in fake crab claim, December 4, 2025.
7. Modern Retail, From Boll & Branch to Bogg, brands are battling a surge of AI-driven return fraud, March 2, 2026.
8. arXiv, Generative AI-Enabled Refund Fraud in Chinese E-Commerce: Investigation on Merchants and Platform Workers, June 2, 2026.
9. Practical Ecommerce, AI makes refund evidence easier to fake, July 16, 2026.
10. Appriss Retail, Appriss Retail Annual Research: Fraudulent Returns and Claims Cost Retailers $103B in 2024, December 30, 2024.
11. National Retail Federation, Consumers expected to return nearly $850 billion in merchandise in 2025, October 15, 2025.
12. Coalition for Content Provenance and Authenticity (C2PA), FAQ, September 2, 2025.
13. arXiv, The Synthetic Media Shift (Chrysidis et al.), April 15, 2026.
14. USPS, File a Claim.
15. USPS, Domestic Mail Manual 609: Filing Indemnity Claims for Loss or Damage.
16. Visa, Visa Dispute Management Guide for Merchants (Condition 13.3, page 61), October 2026.
17. Mastercard, Chargebacks Made Simple Guide, September 2023.
18. Shopify, ReturnReason (GraphQL Admin API).
19. Shopify Help Center, Chargeback reasons.
20. Reuters (via KSL), More retailers adopt 'keep it' returns policies to shelter profits in holiday surge, November 30, 2023.