Paper 2025/1024

Towards Trustless Provenance: A Privacy-Preserving Framework for On-chain Media Verification

Abstract

As generative models continue to evolve, verifying the authenticity, provenance, and integrity of digital media has become increasingly critical—particularly for domains like journalism, digital art, and scientific documentation. In this work, we present a decentralized verifiable media ecosystem for managing, verifying, and transacting authentic digital media using zero-knowledge proofs (ZKPs). Building on VIMz (Dziembowski et al., PETS'25), we extend the framework in three key directions. First, we generalize the model to support arbitrary image regions to achieve selective transformations support such as redaction and regional blurring—features commonly required in privacy-preserving applications. Second, we introduce performance optimizations that yield up to an 18% improvement in off-chain proof generation, and enhance the framework to support cost-efficient on-chain verification. Third, we design and implement a modular smart contract architecture to support a wide range of decentralized media applications. As a flagship use case, we develop a decentralized media marketplace that enables permissionless licensing, ownership transfer, and verifiable attribution. In this setting, users can share transformed media—such as cropped, blurred, or resized previews—alongside ZKPs that prove derivation from a signed original, eliminating the need to trust the seller. Unlike prior fair exchange protocols, which rely on trusted descriptions or encrypted payload delivery, our system enables verifiable public previews and origin-bound proofs without revealing the full content. This approach unlocks new applications beyond marketplaces, including automated infringement dispute resolution and photography contests with verifiable criteria.