NOWA: Null-space Optical Watermark for Invisible Capture Fingerprinting and Tamper Localization

TL;DR

This paper introduces NOWA, a novel optical watermarking technique that embeds invisible, tamper-evident signatures into images during capture, enhancing security and localization without compromising image quality.

Contribution

It proposes a hybrid optical-digital framework with a phase mask and neural reconstruction that provides robust, invisible watermarks for image authentication and tamper detection.

Findings

High-quality image preservation with embedded watermark

Effective tamper localization at pixel level

Resilience against common image degradations

Abstract

Ensuring the authenticity and ownership of digital images is increasingly challenging as modern editing tools enable highly realistic forgeries. Existing image protection systems mainly rely on digital watermarking, which is susceptible to sophisticated digital attacks. To address this limitation, we propose a hybrid optical-digital framework that incorporates physical authentication cues during image formation and preserves them through a learned reconstruction process. At the optical level, a phase mask in the camera aperture produces a Null-space Optical Watermark (NOWA) that lies in the Null Space of the imaging operator and therefore remains invisible in the captured image. Then, a Null-Space Network (NSN) performs measurement-consistent reconstruction that delivers high-quality protected images while preserving the NOWA signature. The proposed design enables tamper localization by projecting the image onto the camera's null space and detecting pixel-level inconsistencies. Our design preserves perceptual quality, resists common degradations such as compression, and establishes a structural security asymmetry: without access to the optical or NSN parameters, adversaries cannot forge the NOWA signature. Experiments with simulations and a prototype camera demonstrate competitive performance in terms of image quality preservation, and tamper localization accuracy compared to state-of-the-art digital watermarking and learning-based authentication methods.