Key Innovations

• DinoHash: Perceptual hashing algorithm robust to common image transformations like filters, compression and crops. Algorithm achieves 12% higher bit accuracy than state-of-the-art methods. Perceptual hash values are signed by the content generator, establishing provenance.
• Privacy-Preserving Queries: Multi-Party Fully Homomorphic Encryption to map image provenance, keeps both user queries and registry data private, with a fallback to MPC if the database is too large.
• Failsafe Detection: Backup classifier identifies synthetic images not found in the registry with state of the art accuracy, showing 25% better classification accuracy on real-world AI generators.
• Adversarial Defense: Protection against both hash collision and hash aversion attacks, that limit the attack surface wherein an attacker cannot modify the provenance without visually changing the image.