ZK-JPEG: Zero-Knowledge Image Editing and Compression
Recorded: Sept. 19, 2026, 8 p.m.
| Original | Summarized |
ZK-JPEG: Zero-knowledge Image Editing and Compression What a lovely hat Cryptology ePrint Archive Papers Updates from the last: Listing by year How to cite Harvesting metadata Submissions Submit a paper About Goals and history Search Advanced search Paper 2026/2039 Camera attestation uses digital signatures to prove an image's provenance from a camera. Lossy compression makes minute changes in order to reduce an image's size, and blurring or redacting regions of an image can protect its subjects. These changes invalidate an image's signature. Prior works use zero-knowledge (ZK) to prove a published image's edit history, but they do not survive lossy encoding such as the JPEG format. We present \zkjpeg, a cryptographic tool for JPEG compression that proves an image was correctly compressed from a secret, committed input. In addition, our tool can verify a large family of image transformations by integrating them into JPEG compression with minimal cost. Our system is fast, flexible, and can be instantiated from off-the-shelf ZK tools. We use PicoZK to convert Python image editing code into a ZK circuit for the line-point zero knowledge (LPZK) proof system. Metadata Available format(s) Category sam @ stealthsoftwareinc comsteve @ stealthsoftwareinc comkimee @ stealthsoftwareinc comjnear @ uvm edu History CC BY BibTeX @misc{cryptoeprint:2026/2039, Note: In order to protect the privacy of readers, eprint.iacr.org |
The proposed work introduces zkjjpeg, a cryptographic tool designed to facilitate JPEG compression while proving the integrity of the image data. This system addresses the challenge of ensuring image provenance by verifying that a digital image originated from a physical camera, a necessity in an environment where deep fake photographs are increasingly prevalent through sophisticated editing tools. Camera attestation typically relies on digital signatures to establish provenance, but these signatures are invalidated by common image transformations, such as lossy compression inherent in the JPEG format, as well as blurring or redaction of image regions. Previous research utilizing zero-knowledge proofs has managed to prove the edit history of published images, but these methods do not maintain validity when subjected to lossy encoding formats like JPEG. To overcome this limitation, the authors developed zkjjpeg to cryptographically prove that an image has been correctly compressed from a secretly committed input. Furthermore, the system is engineered to efficiently verify a wide range of image transformations by integrating these transformations directly into the JPEG compression process with minimal computational overhead. The system is characterized by being fast and flexible, allowing for instantiation using existing zero-knowledge tools. Specifically, the implementation leverages PicoZK to translate Python-based image editing code into a zero-knowledge circuit utilizing the line-point zero knowledge (LPZK) proof system. This integration allows for the verification of complex editing and compression operations within the constraints of the JPEG structure, establishing a verifiable link between the original data, the transformations applied, and the final compressed image. |