Steganographic Passport: An Owner and User Verifiable Credential for Deep Model IP Protection Without Retraining

arXiv:2404.02889v1 Announce Type: cross
Abstract: Ensuring the legal usage of deep models is crucial to promoting trustable, accountable, and responsible artificial intelligence innovation. Current passport-based methods that obfuscate model functionality for license-to-use and ownership verifications suffer from capacity and quality constraints, as they require retraining the owner model for new users. They are also vulnerable to advanced Expanded Residual Block ambiguity attacks. We propose Steganographic Passport, which uses an invertible steganographic network to decouple license-to-use from ownership verification by hiding …

abstract artificial artificial intelligence arxiv capacity constraints cs.cr cs.cv current innovation intelligence legal license ownership protection quality responsible responsible artificial intelligence retraining type usage