Detect fake damaged-on-arrival photos - automated visual forensics for merchants

Detect fake damaged-on-arrival photos - automated visual forensics for merchants targets a $6.0B = 600,000 online merchants x $10,000 ACV. Rationale: global addressable online sellers who pay for enterprise fraud/returns protection; larger merchants likely to pay several K per year for loss reduction and chargeback protection. total addressable market with medium saturation and a year-over-year growth rate of e-commerce fraud and returns tooling demand growing 10-20 percent annually as online returns volumes increase and fraud tooling matures.

Key trends driving demand: Rising return and refund fraud -- increased online shopping and contactless returns expose merchants to more photo-based fake claims.; Image provenance and camera-backed capture adoption -- SDKs and standards for camera-origin proofs allow stronger evidence at capture time.; Platform integrations for post-purchase experience -- marketplaces and platforms centralize returns, creating choke points for integrated verification services.; AI vision and forensic model improvements -- faster, cheaper models enable near-real-time tamper detection at scale..