Paper 2024/1063

VIMz: Verifiable Image Manipulation using Folding-based zkSNARKs

Abstract

With the rise of generative AI technology, the media's credibility as a source of truth has been significantly compromised. This highlights the need to verify the authenticity of media and its originality. Ensuring the integrity of media during capture using the device itself presents a straightforward solution to this challenge. However, raw captured media often require certain refinements or redactions before publication. Zero-knowledge proofs (ZKP) offer a solution by allowing attestation of the correctness of specific transformations applied to an authorized image. While shown to be feasible, previous approaches faced challenges in practice due to their high prover complexity. In this paper, we aim to develop a practical framework for efficiently proving the authenticity of HD and 4K images on commodity hardware. Our goal is to minimize prover complexity by utilizing the folding-based zkSNARKs technique, resulting in VIMz, the first practical verifiable image manipulation system of this kind. VIMz leverages Nova's folding scheme to achieve low complexity recursive zkSNARK proofs of authentic image manipulation. Our implementation results demonstrate a substantial reduction in prover complexity—up to a 3$\times$ speedup in time and a 96$\times$ reduction in memory (from 309 GB in [Kang et al., arXiv 2022] to only 3.2 GB). Moreover, the low memory consumption allows VIMz to prove the correctness of multiple chained transformations simultaneously, further increasing the performance (up to 3.5$\times$). Additionally, we propose a trustless smart contract system that autonomously verifies the proofs of media authenticity, achieving trustless copyright and ownership management, aligning with the standards of the Coalition for Content Provenance and Authenticity (C2PA). Such a system serves as a foundational infrastructure for constructing trustless media marketplaces with diverse applications.