LOCKEY: A Novel Approach to Model Authentication and Deepfake Tracking

TL;DR

LOCKEY introduces a combined key-based authentication and watermarking system for generative models, effectively deterring unauthorized deepfakes and enabling user identification even with full model access.

Contribution

This work presents a novel integrated approach that uses user-specific keys for model authentication and watermarking for user tracking in generative models.

Findings

Effective detection of invalid keys during inference.

Successful embedding of watermarks in audio models.

Robustness of watermarks against distortions.

Abstract

This paper presents a novel approach to deter unauthorized deepfakes and enable user tracking in generative models, even when the user has full access to the model parameters, by integrating key-based model authentication with watermarking techniques. Our method involves providing users with model parameters accompanied by a unique, user-specific key. During inference, the model is conditioned upon the key along with the standard input. A valid key results in the expected output, while an invalid key triggers a degraded output, thereby enforcing key-based model authentication. For user tracking, the model embeds the user's unique key as a watermark within the generated content, facilitating the identification of the user's ID. We demonstrate the effectiveness of our approach on two types of models, audio codecs and vocoders, utilizing the SilentCipher watermarking method. Additionally, we assess the robustness of the embedded watermarks against various distortions, validating their reliability in various scenarios.