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AI image tools make it possible to create convincing visuals in seconds, with little design experience or expensive equipment. That is a major advantage for legitimate creative work—and a lower barrier for fabricated product photos, receipts, identities and damage claims. The defensible conclusion is narrower than “AI has caused billions in retail losses”: current sources document a specific $1.5 million returns-abuse operation and broader fraud totals, but no audited global figure for AI-generated retail-image abuse.
What AI image tools change for creators and criminals
Modern image generators can produce multiple polished images from a text prompt. Jason Grunberg, Forter’s chief marketing officer, described that accessibility in a TechRadar Pro Perspectives article published 12 August 2026. His point is practical rather than a comparative capability test: a person without formal design training can now make visual material quickly.
For legitimate users, that means faster concept art, campaign mock-ups, product illustrations, storyboards, social graphics and variations on an existing idea. A small business can explore visual directions before hiring a designer; a teacher can create an illustration for a lesson; a developer can prototype a game asset. The same convenience removes friction from deception. Someone trying to support a false refund claim no longer needs advanced retouching skills to create a plausible image of a damaged item.
The legal and ethical question is how synthetic content is used. The FBI’s Internet Crime Complaint Center said in its 3 December 2024 advisory that synthetic content is not inherently illegal. It can, however, facilitate crime when it is used to misrepresent identity, products, events or transactions.
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How AI-generated imagery can support retail abuse
False damage and refund claims
A dishonest claimant might submit an AI-altered photograph showing a cracked screen, torn fabric, water damage or another defect that did not exist when the item was delivered. Similar imagery can be paired with a realistic-looking receipt, shipping label or delivery scene. The image is not necessarily the whole fraud; it is supporting material designed to make a claim appear consistent.
Grunberg’s article distinguishes coordinated groups from opportunistic shoppers. A coordinated operation may reuse methods across many accounts, devices or merchants. An individual customer may exploit a generous policy once. In both cases, intent can be difficult to infer from one photograph, especially when a legitimate customer has edited, compressed or re-photographed an image before uploading it.
Counterfeit listings and non-delivery promotions
The FBI advisory gives examples beyond returns: synthetic profile pictures and identity documents, promotions for counterfeit goods or products that will never be delivered, and fabricated imagery used to make a seller or offer look established. An image can create credibility even when the underlying business, inventory or shipping record is false.
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Official FBI examples also include fake disaster or conflict scenes used to solicit donations, manipulated material intended to influence markets, and sexual-extortion images. These cases matter to retailers because the same generation tools, accounts and payment infrastructure can be reused across different kinds of fraud. Retail-image abuse is one application of a broader synthetic-media problem, not a separate technology category.
What the published numbers do—and do not—show
Several statistics are useful only when their scope is kept intact. The figures below should not be combined into a single estimate of AI-driven retail losses.
| Figure | Source and date | What it measures | Important limit |
|---|---|---|---|
| 57% | Attributed by Jason Grunberg’s TechRadar article to the Merchant Risk Council (2026) | Merchants reportedly saying refund and policy abuse increased over the prior year | The underlying survey’s timing, respondent count and geography were not established; the figure does not measure AI use |
| $1.5 million | Forter account reported in the 12 August 2026 TechRadar article | One returns-abuse operation stopped over Christmas 2025; AI-altered damage images were part of the method | A vendor-reported incident, not an audited market total |
| More than $12.5 billion | Federal Trade Commission, March 2025 | Consumer fraud losses reported in the United States during 2024 | Includes all reported consumer fraud, not specifically retail returns, image generation or AI |
| $2.95 billion | Federal Trade Commission, March 2025 | Reported imposter-scam losses in 2024 | Also a broad category, not an AI-image or retail measure |
| More than 99% | Amazon 2024 Brand Protection Report, published 2025 | Suspected infringing listings Amazon says it blocked before a brand reported them | Amazon’s own marketplace result, not an independent benchmark |
| More than 15 million | Amazon 2024 Brand Protection Report, published 2025 | Counterfeit products Amazon says it identified, seized and disposed of worldwide in 2024 | Company-reported enforcement activity; it does not isolate AI-generated images |
| Around 35% fewer | Amazon 2024 Brand Protection Report, published 2025 | Valid infringement notices submitted by brands since 2020, according to Amazon | Amazon attributes the decline partly to more listing attempts being blocked proactively |
The FTC’s more than $12.5 billion total is sometimes repeated as if it proved billions of dollars in AI-generated retail abuse. It does not. The FTC lists online-shopping problems among reported fraud categories, but its total does not identify the image-generation method or a returns-abuse subset. Likewise, the $1.5 million Forter case demonstrates a real reported operation without establishing prevalence across the industry.
Why a damage photograph cannot be the sole decision signal
Image-only screening creates two risks. First, generation and editing quality is improving, so a visual reviewer may miss a fabricated scene. Second, legitimate evidence is messy: a phone may apply automatic enhancement, a customer may crop an image, and a damaged parcel may be photographed under poor lighting. Rejecting every file with compression artifacts would punish honest claims.
Grunberg’s recommendation is to identify the bad actor rather than penalize the good customer. He proposes evaluating identity, device behavior, transaction history, account age and claim patterns together, and warns against relying on visual evidence by itself. That is a vendor executive’s recommendation, not an independently validated standard, but it provides a sensible design principle: treat the image as one signal in a wider investigation.
A practical, layered workflow for retailers
1. Collect consistent evidence without making it impossible to claim
- Give customers a clear upload path inside the authenticated order or returns flow.
- Ask for more than one angle when the issue is difficult to verify, such as a concealed crack or missing component.
- Request the package, shipping label and product identifier when those details are relevant.
- Preserve the original upload and basic file information, but do not automatically reject a claim because metadata is missing; many messaging and social applications remove it.
- Explain what information is required and why, so a legitimate customer can correct an incomplete submission.
2. Use visual checks as triage, not proof
Automated review can flag inconsistent shadows, reflections, perspective, repeated textures, impossible edges, mismatched text or a product appearance that conflicts with catalog imagery. File-level checks can identify duplicate or near-duplicate submissions. These checks are useful for prioritizing review, but no single “AI detector” should be treated as a final verdict without validation on the retailer’s own images and claim types.
3. Add identity, device and transaction context
- Compare the claimant’s verified identity and payment details with the order record.
- Look at account age, prior successful purchases, previous claims and the timing of new accounts.
- Assess device and session patterns, including repeated use across accounts where the retailer is legally and technically able to do so.
- Check whether the same image, wording, address or shipping pattern appears in other claims.
- Compare the claimed damage with fulfillment scans, carrier records, serial numbers and return-receipt data.
Cross-account and cross-merchant coordination can be informative, but it requires appropriate data governance and, where relevant, cooperation between businesses. The TechRadar article describes that rationale; the sources do not provide an independent measurement of how much any one signal improves detection.
4. Route cases by risk and keep a human appeal path
- Approve straightforward, low-risk claims quickly when order and evidence details agree.
- Request clarification for conflicting but plausibly innocent cases.
- Escalate repeated, coordinated or high-value anomalies for trained human review.
- Record the reason for a decision, the evidence considered and the customer’s response.
- Offer an appeal or alternative inspection route instead of permanently labeling a customer from one disputed image.
Human review is particularly important when the consequence is account closure, a denied warranty or a chargeback dispute. A model can prioritize work; it should not silently convert an uncertain visual score into a final accusation.
How marketplace operators combine signals
Amazon’s 2024 Brand Protection Report describes one large marketplace approach. Amazon says its controls combine computer vision and large language models with listing text, visual attributes, pricing patterns and other signals. It also reports seller-identity verification, ongoing account monitoring and human review when an investigation requires it.
Those methods illustrate a broader comparison framework:
| Defense question | Narrow approach | Stronger design goal |
|---|---|---|
| Signal range | Image-only review | Visual evidence combined with identity, device, account, transaction and claim-pattern information |
| Review process | Automated rejection | Machine screening followed by documented human investigation for consequential cases |
| Context | Each claim assessed in isolation | Repeated activity and possible coordination examined across accounts or merchants where lawful |
| Customer impact | Blanket policy tightening | Targeted friction for high-risk cases while preserving a simple path for ordinary customers |
Amazon’s reported outcomes are not a neutral head-to-head test of fraud products. They show what one retailer says its own systems achieved, not a universal performance level that every merchant should expect.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keeping fraud controls fair to legitimate customers
Returns policies exist because products can genuinely arrive damaged, fail in use or differ from their description. A retailer that responds to synthetic-media risk by demanding extensive proof from everyone may reduce abuse while increasing abandonment, support costs and reputational harm.
Use graduated friction instead: a first claim with a consistent order history should not receive the same treatment as a cluster of high-value claims tied to the same device and address. Tell customers what will happen next, give them a way to submit missing information, and separate a temporary investigation from a final finding of fraud. Measure both prevented loss and legitimate claims incorrectly delayed or denied.
What consumers and creators should do
If you are submitting a genuine damage claim
- Photograph the package, label, product and damage as soon as practical.
- Keep original files and explain any edits, such as cropping for upload.
- Use the retailer’s authenticated support channel rather than sending identity documents to an unverified contact.
- Describe what happened accurately; do not add damage because a form appears to expect it.
If an offer or image looks suspicious
- Verify the seller through an independently obtained website or marketplace record, not only the link in a message.
- Be cautious when a dramatic image is paired with urgency, an unusually low price or a request for payment outside the platform.
- Do not send passwords, one-time codes or identity documents to prove interest in a product.
- Report suspected counterfeit, non-delivery, impersonation or sextortion activity to the relevant platform and authorities.
Creators using synthetic imagery should label it when an audience could reasonably mistake it for a photograph or documentary record. Clear disclosure protects viewers and reduces the chance that a fictional scene is reused as supposed evidence.
The defensible bottom line
AI image tools are a genuine creative multiplier and a genuine fraud enabler. The documented $1.5 million returns-abuse operation shows how altered damage images can support a larger scheme, while the FBI’s examples show that the risk extends to identities, counterfeit promotions, donations, markets and sextortion. But neither that case nor the FTC’s more than $12.5 billion in reported 2024 consumer-fraud losses proves a multi-billion-dollar total caused by AI-generated retail images.
Retailers should therefore invest in layered, explainable controls: consistent evidence collection, visual triage, identity and transaction context, coordinated-activity analysis and human review. The objective is not to distrust every image or make every honest customer prove innocence. It is to detect patterns of abuse while preserving a workable path for legitimate people.
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