How to detect fake return photos

A faked claim photo can be an unrelated image pulled from the web, a real photo edited to add or exaggerate damage, a picture of a screen showing another image, or a fully synthetic image generated by an AI model. The more convincing ones look completely normal — which is exactly why visual inspection alone is unreliable.

Metadata: EXIF and maker fields record how and when an image was created; editing software and inconsistent timelines leave fingerprints. Provenance: content-credential signatures (where present) state an image's origin and edit history. Pixel forensics: error-level analysis, noise consistency and edge continuity reveal local edits. Reuse: reverse-image search surfaces photos lifted from the web or submitted before. AI detection: an independent model flags hallmarks of generated imagery.

Because each signal can be individually defeated, Claimscan evaluates them together and reports a combined manipulation likelihood rather than a single yes/no.

The fastest path is to drop the customer's photo into Claimscan and read the indicator report in seconds — no warehouse or checkout integration required. The verdict is advisory: a SUSPICIOUS or LIKELY_MANIPULATED result is a prompt to ask for a second photo or a short video, never an automatic refusal. Treating the output as a decision aid keeps genuine customers happy while catching the obvious fakes.