When do marketplaces need to detect AI generated product images?
You need to check product photos whenever the photo is the main promise a seller makes and the buyer cannot inspect the item before paying. Marketplace integrity teams, trust and safety reviewers, category managers and independent shop owners all run into the same situations.
- Listings for items that do not exist: furniture, gadgets or clothing shown in flawless studio scenes, often from new accounts with dozens of listings.
- Secondhand and resale listings where a generated “real” photo hides the true condition of a used item.
- Handmade and custom goods, where buyers pay for craft they expect to see in the photos.
- Review sections filled with generated text and generated “customer photos”.
- Seller onboarding, where a storefront photo, a selfie or a document image is part of seller verification.
- Ads that send buyers to an off-platform shop built in a weekend with generated pages.
Not every generated image is a problem. A real product photo placed on an AI-made background, or a clearly labeled concept render for a pre-order, can be honest. Your rules should target images that misrepresent what the buyer will receive.
What are the risks of fake product photos and AI reviews on a marketplace?
Missing fake product photos costs buyers money and costs the platform trust. A buyer who receives something that looks nothing like the listing asks for a refund, files a chargeback or leaves, and often blames the marketplace rather than the seller.
AI reviews on a marketplace carry legal weight as well. The US Federal Trade Commission finalized a rule on fake reviews and testimonials in August 2024, effective October 21, 2024. It bans writing, buying or selling fake reviews, including reviews generated by AI, and lets the FTC seek civil penalties against knowing violators.
The opposite error hurts honest sellers. Many small shops use AI tools to remove backgrounds, fix lighting or retouch dust, and some use 3D renders for catalog shots. A heavily processed real photo can come back uncertain or even probably AI. Delisting that seller on a score alone removes a legitimate business and invites appeals you will lose.
What should a listing image policy say about AI generated listings?
A good listing image policy separates acceptable editing from misrepresentation, so sellers know the rules before a reviewer applies them. Here is a simple structure you can adapt.
| Image type | Example | Typical rule |
|---|---|---|
| Real photo, AI-assisted editing | Background removed, lighting corrected, dust retouched | Allowed |
| Real product, AI-made scene | A real lamp photo composited into a generated living room | Allowed if the product itself is unchanged |
| Concept or render | A design not yet manufactured, shown for pre-order | Allowed only with a clear label |
| Generated image shown as the real item | A generated photo of a used jacket “in excellent condition” | Not allowed |
| Generated review photo | A “customer photo” that no customer took | Not allowed |
How to detect AI generated product images: a review workflow
The workflow below starts with signals that cost nothing to check, uses detection on the cases that remain, and ends with a human decision. Most problem listings show warning signs before anyone opens a detector.
- 01Read the listing context: account age, number of listings created at once, prices far below market, and identical photos reused across several sellers.
- 02Run a reverse image search on the main photo. Stolen catalog photos usually show up; a generated photo usually has no earlier copy anywhere.
- 03Inspect the product itself. Check printed labels, logos and care tags for warped letters, and check that buttons, ports, seams and handles sit where they would on a real object.
- 04Compare all photos in the listing. A real item keeps the same shape, color, pattern and number of parts from every angle; generated sets often drift between shots.
- 05Run the clearest photo through the AI image detector and read the listed signals, not just the verdict.
- 06Ask the seller for proof: the item next to a handwritten note with today’s date and their username, or a short video turning the item in their hand.
- 07Decide with a person, record the reasons, and give the seller a way to appeal.
The guide on how to tell if an image is AI-generated explains which visual clues still hold up and which ones newer generators have fixed.
AI Photo Detector · Free plan
Check a listing photo for content credentials, AI-image signals and visible anomalies such as warped lettering.
Spotting AI reviews on a marketplace
Generated reviews are easier to catch by behavior than by wording. Look for bursts of five-star reviews in a short window, reviewers with no other history, and reviews that repeat the listing’s own feature list without describing any use of the product. The guide on how to spot AI-generated reviews covers these patterns in detail.
For longer reviews, the AI text detector needs at least 300 characters and shows which passages look generated. Treat it as one input: a real customer may have used a writing tool to polish an honest review.
A proof request honest sellers can meet
A proof request works when a genuine seller can answer it in a few minutes with a phone. Keep it specific and the same for everyone in the same category.
- One photo of the item next to a paper showing the date and the seller’s username.
- One close-up of a detail generators get wrong: a label, a serial plate, a stitched logo.
- For higher-value items, a short video turning the item, which the deepfake video detection tool can also screen.
- A clear deadline, and a note that the listing stays paused, not deleted, until then.
Seller verification and AI images
During onboarding, a storefront photo or a selfie can be generated too. AnyFact can flag signs of generation in those images, but it is not an identity verification provider: it does not match faces, run liveness checks or validate ID documents. The page for fraud and KYC teams explains where it fits next to those controls.
Which AnyFact detectors fit marketplace teams, and what do they return?
Three detectors cover what marketplace teams review. Each returns a verdict of probably human, uncertain or probably AI (text and websites can also be mixed), the signals behind it, a plain-language explanation, the limits of the method and a PDF report you can attach to an enforcement case.
| What you review | Detector | What it returns |
|---|---|---|
| Listing or review photo | Photo (JPG, PNG, WebP, HEIC up to 20 MB, or a link) | Provenance findings, a classifier verdict and visible anomalies in lettering, reflections and textures |
| Long review or product description | Text (300 characters or more) | A verdict per passage of 2–3 sentences, with generated passages highlighted |
| Off-platform shop linked from an ad | Website (any public URL) | Up to 20 pages checked for generated text and images, plus domain age and Google Safe Browsing where available |
The AI website checker suits dropshipping storefronts: a recently registered domain whose pages are mostly generated is a strong reason to look harder at the sellers sending traffic there. The guide on how to tell if a website is AI-generated lists the manual checks.
AnyFact works through its web app and has no public API yet. It fits escalated listings, appeals and sample audits rather than scanning every new upload.
AI Website Checker · Free plan
Check a linked storefront for generated pages and images, with domain age and safety signals.
What an AI image check cannot tell you about a listing
An AI image check cannot tell you whether a real photo shows the item being sold. A genuine photo of a different unit, an old photo of an item since damaged, or a manufacturer image used by a reseller will all come back probably human, and each can still mislead a buyer.
Detection is also weaker on small, recompressed thumbnails and on clean product shots against a plain white background, which give a classifier little texture to work with. Always check the largest original image the seller uploaded, and let the proof photo and the seller’s history carry most of the weight.
Frequently asked questions
Can you tell if a product photo is AI generated?
Often, but not always. Warped lettering on labels, parts that change between angles and provenance data naming a generator are strong clues, and an AI image detector adds a classifier verdict. The most reliable test is a proof photo or short video of the real item from the seller.
Are AI generated product images allowed on marketplaces?
It depends on each marketplace’s rules, so check them. Many allow AI-assisted editing of real photos and draw the line at images that misrepresent the item the buyer receives. If you run a marketplace, write that line down in your listing policy.
How do I spot AI reviews on a marketplace?
Look at behavior first: bursts of reviews in a short window, new accounts, near-identical structure and no detail about actually using the product. A text detector can add a signal on reviews longer than 300 characters.
Can AnyFact scan every new listing automatically?
Not yet. AnyFact has no public API and works through its web app, which suits reviewing escalated listings, appeals and samples rather than automated screening.
How much does it cost to check a listing?
AnyFact has a free plan. Signing up is free and gives you 10 credits a month, no credit card needed. A photo or text check costs 1 credit and a full website check costs 10 credits.