A brand's product photos are usually its most copied asset. They appear on counterfeit listings, rival stores, and wholesale catalogues within days of a launch, because good photography is expensive and stealing it is free. Teams respond with watermarks, then discover the watermarks cropped out and the photos back in use.
The honest starting point is that prevention and proof are different jobs. Prevention raises the thief's effort a little. Proof, kept quietly and completely, is what wins takedowns and disputes. This guide covers both, in the order that actually matters.
A verification page only works if codes are unpredictable, checked against a real database, and rate limited. A page that validates format alone teaches counterfeiters that any plausible code passes, which is worse than no page at all.
A common build validates that a code has the right length and characters, then congratulates the visitor. Copy makers respond by printing invented codes in the same format on every fake. Customers check, see a success message, and trust a counterfeit more than they would have without the page.
The second failure is unlimited attempts. Without rate limits and logging, a checker becomes an oracle that lets sellers test which code ranges are valid, harvest live codes, and print real ones across thousands of copies. Every public checker needs throttling and duplicate detection from day one.
Generate codes with enough randomness that guessing is impractical, store them server side, and bind each code to a product and batch. The result page should show the product name, variant, and sale region, so a code copied onto the wrong product exposes itself. A code checked in two countries or fifty times should flag, not just pass.
Handle failure helpfully and vaguely: explain that the code was not recognised, show how to find the genuine code location, and route the visitor to support with the listing details. Do not reveal whether the code was nearly right. Log every check with time and rough location; that log is enforcement intelligence.
The check log maps the fake trade better than listing scans do. Clusters of failed codes from one region, or one valid code checked hundreds of times, point at specific sellers and batches. Feed that into marketplace reports and test purchases, and the verification page becomes a sensor network your customers staff for free.
Publish the checker where buyers actually look: packaging, the product page, and post purchase emails. A verification tool nobody finds protects nobody, and every genuine check you collect sharpens the contrast that exposes the copies.
Keep reading: Who we protect covers a nearby case in this series.
The exact product, variant, and batch or region the code belongs to, plus a check count warning if that code has been verified many times before. Showing specifics lets a buyer spot a code reused on the wrong product.
Yes, and they do. That is why duplicate detection matters: one code checked in many places or far beyond plausible volume is evidence of copying. The system should flag repeats rather than failing the first buyer who checks.
Long and random enough that guessing valid codes is unrealistic, while still typeable by a customer. Grouped characters, a check digit to catch typos, and server side validation are the standard pattern. Sequential numbering alone is an invitation.
Keep the product and packaging, photograph the code and the item, and contact the brand through its own site with the seller details. A failed code does not prove a fake by itself, since codes get misread, but the brand can resolve it and use the report.