how to spot AI photos in online listings

do two checks before any money moves

run the photos through two filters before you send a deposit or click buy. reverse-image-search them to catch images lifted from a real listing somewhere else, and run a generation check to catch the ones a model invented. then read both against how the seller behaves. no single check settles it, which is exactly why you stack them: stolen photos and synthetic photos fail different tests.

why listings fill up with fake photos

a listing is a soft target because a photo is often the only proof a buyer ever sees. New York’s Department of State issued a trend alert on November 13, 2025 warning of a significant rise in artificially generated pictures on real estate listings, and noting they can constitute deceptive advertising under Real Property Law § 441-c.

the money behind it is large and rising. US consumers reported losing more than $12.5 billion to fraud in 2024, up 25% over the year before, and online shopping was the second most-reported category (FTC, March 2025). in 2025, shopping was the single most-reported scam that started on social media, and more than 40% of those losses came from ordering something seen in an ad, often on a site dressed up as a known brand (FTC, April 2026).

stolen photos drove this long before AI did. the Better Business Bureau reports that more than 5 million people have lost money to rental scams, with a median loss near $400 and one in three victims losing more than $1,000. an NYU analysis of roughly 2 million Craigslist ads turned up 29,000 scam listings, most of them reusing photos and descriptions copied from real listings.

the visual tells in a listing photo

the tells are real, but they fade with every model release, so read them as hints rather than proof. the New York alert flags four to start with:

• →window views. the scene outside one window doesn’t match the next, or a view makes no sense for the building.
• →backgrounds. blurry, smeared or impossible spaces behind the room that don’t resolve on a closer look.
• →architectural details. trim, cabinetry and fixtures that warp, bend or melt into the walls.
• →stray watermarks. a generator’s mark left in a corner the seller forgot to crop.

commonly cited hints add a few more: straight lines that bow, textures that smear into mush away from the focal point, signage and house numbers that turn to gibberish, and lighting too even to be a phone snap. each new image model trains another of these away, so a clean photo no longer proves a real room.

the tells that aren’t in the photo

the image rarely travels alone. the listing around it leaks as much as the pixels do:

• →a brand-new account. no posting history, no reviews, a profile created days ago.
• →a price under everything comparable. the deal that exists to rush you past your own caution.
• →a photo with the wrong footprint. found on many unrelated listings (lifted) or nowhere else online at all (often invented).
• →no live look. refusal to do a video walkthrough or an in-person viewing, with a story for why.
• →off-platform pressure. a push to move to WhatsApp or Telegram and to wire a deposit, send a gift card, or pay in crypto before you’ve seen the place.

why reverse image search isn’t enough on its own

reverse search only finds photos that already exist somewhere. the FTC’s standard advice is to reverse-image-search a listing’s photos, and it works: a deposit-scam photo copied from a real rental will surface its original. but a fully AI-generated photo was never a real photo of a real place, so there’s nothing online to match. a blank result proves nothing. it’s the cue to run a generation check, the question reverse search can’t answer. in a documented 2025 case, a careful searcher ran reverse searches and was still deceived because the AI image was convincing enough (Content Authenticity Initiative).

what amige. does

amige. routes each scan to the detectors strongest for that image, then runs a panel of independent detectors built by different teams and returns a probabilistic estimate of whether the photo looks AI-generated. it adds a best guess at the family the image resembles, like “looks like a diffusion render”, always with the question mark, and returns “uncertain” when the reads conflict rather than forcing a call. as image generators change, the panel gets recalibrated. the full method, limits included, sits on the machine.

where this falls short

generation detection carries real limits. the main ones:

• →false positives happen. a genuine listing photo can read as AI, especially after heavy editing, virtual staging or upscaling. one number won’t carry a deposit decision.
• →screenshots and re-saves hurt. a screenshotted or recompressed photo strips provenance metadata and weakens classifier signals. scan the original file when you can.
• →the tells decay. as diffusion models improve, every visual cue gets weaker, which is the argument for a model-level estimate over any single artifact.
• →two different jobs. a generation check won’t flag a real-but-stolen photo, and a reverse image search won’t flag an AI-made one. run both.

either way the verdict is an estimate, not proof. read it as the start of the question, not the end of it.