ZK-IMG: Attested Images via Zero-Knowledge Proofs to Fight Disinformation

Authors: Daniel Kang, Tatsunori Hashimoto, Ion Stoica, Yi Sun

Published: 2022-11-09 10:02:20+00:00

AI Summary

ZK-IMG is a library that attests to image transformations without revealing the original image, addressing limitations of previous work by using zero-knowledge proofs and optimizing for high-resolution images. It allows developers to specify image transformations, which are compiled into ZK-SNARKs, enabling verification of HD images on commodity hardware.

Abstract

Over the past few years, AI methods of generating images have been increasing in capabilities, with recent breakthroughs enabling high-resolution, photorealistic deepfakes (artificially generated images with the purpose of misinformation or harm). The rise of deepfakes has potential for social disruption. Recent work has proposed using ZK-SNARKs (zero-knowledge succinct non-interactive argument of knowledge) and attested cameras to verify that images were taken by a camera. ZK-SNARKs allow verification of image transformations non-interactively (i.e., post-hoc) with only standard cryptographic hardness assumptions. Unfortunately, this work does not preserve input privacy, is impractically slow (working only on 128$times$128 images), and/or requires custom cryptographic arguments. To address these issues, we present zk-img, a library for attesting to image transformations while hiding the pre-transformed image. zk-img allows application developers to specify high level image transformations. Then, zk-img will transparently compile these specifications to ZK-SNARKs. To hide the input or output images, zk-img will compute the hash of the images inside the ZK-SNARK. We further propose methods of chaining image transformations securely and privately, which allows for arbitrarily many transformations. By combining these optimizations, zk-img is the first system to be able to transform HD images on commodity hardware, securely and privately.