Onion-Hash: A Compact and Robust 3D Perceptual Hash for Asset Authentication

Abstract

The digitalization of manufacturing processes and recent trends, such as the Industrial Metaverse, are continuously increasing in adoption in various critical industries, resulting in a surging demand for 3D CAD models and their exchange. Following this, it becomes necessary to protect the intellectual property of content designers in increasingly decentralized production environments where 3D assets are repeatedly shared online within the ecosystem. CAD models can be protected by traditional security methods such as watermarking, which embeds additional information into the file. Nevertheless, malicious actors may find ways to remove the information from a file. To authenticate and protect 3D models without relying on additional information, we propose a robust 3D perceptual hash generated based on the prevalent geometric features. Furthermore, our geometry-based approach generates compact and tamper-resistant fingerprints for a 3D model by projecting multiple spherical sliced layers of intersection points into cluster distances. The resulting hash links the 3D model to an owner, supporting the detection of counterfeits. The approach was benchmarked for similarity search and evaluated against established state-of-the-art shape retrieval techniques. The results show promising resistance against arbitrary transformations and manipulations, with our approach detecting 25.6% more malicious tampering attacks than the baseline.