Paper 2026/641

HyperVerITAS: Verifying Image Transformations at Scale on Boolean Hypercubes

Abstract

We present $\mathsf{HyperVerITAS}$, a new zero-knowledge proof (ZKP) system for image provenance that enables scalable, efficient, and privacy-preserving verification of image transformations. $\mathsf{HyperVerITAS}$ builds upon the same minimal trust model as $\mathsf{VerITAS}$ (IEEE S&P '25), requiring trust only in the image source device, while treating the editing software as untrusted. Unlike $\mathsf{VerITAS}$, which relies on FFT-intensive SNARKs and suffers from high memory overhead (up to 120 GB), $\mathsf{HyperVerITAS}$ leverages multilinear polynomial encodings over the Boolean hypercube to dramatically reduce both proving time and memory usage. Our design cleanly separates signature verification from image transformation, supports modular integration of multiple polynomial commitment schemes (including post-quantum constructions) and naturally extends to a wide range of affine image transformations. We implement $\mathsf{HyperVerITAS}$ with two distinct commitment schemes (Brakedown and multilinear KZG) and evaluate it on full-system pipelines involving cropping and grayscaling. On commodity hardware (Apple M3, 36 GB RAM), $\mathsf{HyperVerITAS}$ generates proofs for 33 MP images using only 27 GB of RAM and 6.6 minutes of proving time, whereas $\mathsf{VerITAS}$ fails to scale beyond 4 MP. These results establish $\mathsf{HyperVerITAS}$ as a practical and scalable ZKP system for secure and efficient image provenance.