We’ve had problems with deepfake celebrity scams, non-consensual deepfake sexual material, and deepfake politicians. Now we have to deal with… deepfake plants?
Yup, AI seed slop is now a thing. 404 Media documented scammers marketing seeds for plants that supposedly bloom in the shape of birds, butterflies, and cat heads, complete with technicolor leaves and impossible color gradients. The listings appear on eBay, Amazon, and Etsy, and the platforms are struggling to keep up. The plants never existed, except as some get-rich-quick merchant’s fever dream.
Don’t confuse this seed scam with the unsolicited seed incident that the FTC warned about in 2020. In that case, people received packets of seeds in the mail that they never ordered.
That was a “brushing” scam, in which scammers would send out low-value packets of seeds to unwitting recipients. This allowed them to register those people as verified purchasers on ecommerce platforms so they could use their accounts to create fake reviews for likely fake products. The confused “customers” were left none the wiser.
Those scammers were after verified purchaser status. This time around, the seeds themselves are the cash grab. This means they need to look visually intriguing and appealing.
AI makes that much easier. A scammer who once needed to Photoshop a fantasy flower onto a real photograph can now type a prompt into an AI engine and get a photorealistic bloom in seconds. That means they can generate hundreds of these nonsensical flora images with next to no effort.
Scamming victims for a few bucks at a time
That AI capability lets scammers level up low-value rip-offs with ease. We don’t imagine that people are handing over their life savings for these seeds. Sure enough, after 20 seconds of searching, we found a dubious post on a popular ecommerce platform from a seller in Shenzhen offering cat-face flower seeds for the equivalent of around $5.75.
Even if someone orders multiple packets, we still imagine most individual seed scams will fall within the 23% of fraud incidents that Gallup says involve losses of under $100. While crypto investment scams often deliver big payoffs for criminals, there’s clearly money to be made from high-volume, low-value ecommerce scams too.
Oh, why did we find the seller’s ad to be suspicious? Here’s why:
Yes, cat-faced orchids are technically a real thing, which makes things trickier. The petals can resemble whiskers, and the markings can look a bit like eyes and a nose. But they actually look like the one below, not the deranged, psychedelic wonder above. If you think that’s an actual flower, we have some magic paperclips to sell you.
What buyers can actually do
Detection is still visual, not forensic. No stamp guarantees a flower is real. A few things do help:
- The most effective check is common sense. Just because something melts your heart doesn’t mean it should open your wallet. Once again, we refer you to the picture above. So take a breath and ask yourself if it really seems legit.
- Look up the botanical Latin name before ordering. If the variety doesn’t appear in a university extension database, the RHS Plant Finder, or a reputable specialty nursery’s catalog, treat the listing with extreme skepticism.
- Buy from named seed companies with a physical address in your country, published germination rates, and a history longer than the current gardening season.
- If you do want to take a chance with an overseas seller, check for a mention of import certificates. Seeds imported into the United States generally need either a phytosanitary certificate or, for qualifying small quantities, a USDA APHIS PPQ 587 permit.
The individual damage here is small, but it’s a preview of what cheap generative AI does to any market that relies on trusting product photos.
Scammers can now spin up a store, generate an entire catalog of impossible products, and disappear before the first shoots of what turns out to be boring old rosemary show themselves in your garden. Now’s the time to be extra-skeptical about ecommerce listings that rely on stunning visuals as proof.
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