Scalable Dual Fingerprinting for Hierarchical Attribution of Text-to-Image Models

The commercialization of generative artificial intelligence (GenAI) has led to a multi-level ecosystem involving model developers, service providers, and consumers. Thus, ensuring traceability is crucial, as service providers may violate intellectual property rights (IPR), and consumers may generate harmful content. However, existing methods are limited to single-level attribution scenarios and cannot simultaneously trace across multiple levels. To this end, we introduce a scalable dual fingerprinting method for text-to-image (T2I) models, to achieve traceability of both service providers and consumers. Specifically, we propose 2-headed Fingerprint-Informed Low-Rank Adaptation (FI-LoRA), where each head is controlled by a binary fingerprint and capable of introducing the fingerprints into generated images. In practice, one FI-LoRA head is used by the developer to assign a unique fingerprint to each service provider, while the other is made available to service providers for embedding consumer-specific fingerprints during image generation. Our method does not merely embed two fingerprints within the generated image but instead allows independent control over them at developer level and business level, enabling simultaneous traceability of businesses and consumers. Experiments show that our method applies to various image generation and editing tasks of multiple T2I models, and can achieve over 99.9% extraction accuracy for both fingerprints. Our method also demonstrates good robustness against both image-level attacks and white-box model-level attacks. We hope our work provides a unified solution for developers to implement multi-tiered traceability of their models and hierarchical control over model distribution and content generation.