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Over the past few years, AI methods of generating images have been increasingin capabilities, with recent breakthroughs enabling high-resolution,photorealistic "deepfakes" (artificially generated images with the purpose ofmisinformation or harm). The rise of deepfakes has potential for socialdisruption. Recent work has proposed using ZK-SNARKs (zero-knowledge succinctnon-interactive argument of knowledge) and attested cameras to verify thatimages were taken by a camera. ZK-SNARKs allow verification of imagetransformations non-interactively (i.e., post-hoc) with only standardcryptographic hardness assumptions. Unfortunately, this work does not preserveinput 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 imagetransformations while hiding the pre-transformed image. zk-img allowsapplication developers to specify high level image transformations. Then,zk-img will transparently compile these specifications to ZK-SNARKs. To hidethe input or output images, zk-img will compute the hash of the images insidethe ZK-SNARK. We further propose methods of chaining image transformationssecurely and privately, which allows for arbitrarily many transformations. Bycombining these optimizations, zk-img is the first system to be able totransform HD images on commodity hardware, securely and privately.

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