PCDiff: Proactive Control for Ownership Protection in Diffusion Models with Watermark Compatibility

2025-04-16

AI Summary

Protecting intellectual property in text-to-image diffusion models remains challenging due to the lack of mechanisms enforcing ownership during generation. Method: This paper proposes the first proactive access control framework, embedding a trainable fusion module within the decoder and integrating a hierarchical encryption-based authentication mechanism—thereby tightly coupling output fidelity with user authorization status: only users possessing valid credentials can reconstruct high-fidelity images; unauthorized access triggers controlled quality degradation. The architecture adopts a decoupled design, ensuring native compatibility with mainstream watermarking and post-processing techniques, thus transcending conventional passive detection paradigms. Results: Extensive experiments demonstrate that credential verification remains highly correlated with image quality under diverse attacks (authorization accuracy >99.2%), with strong robustness. To our knowledge, this is the first method enabling proactive, generation-time ownership enforcement in diffusion-based image synthesis.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Security and Privacy: Authentication, authorization, and access control
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

With the growing demand for protecting the intellectual property (IP) of text-to-image diffusion models, we propose PCDiff -- a proactive access control framework that redefines model authorization by regulating generation quality. At its core, PCDIFF integrates a trainable fuser module and hierarchical authentication layers into the decoder architecture, ensuring that only users with valid encrypted credentials can generate high-fidelity images. In the absence of valid keys, the system deliberately degrades output quality, effectively preventing unauthorized exploitation.Importantly, while the primary mechanism enforces active access control through architectural intervention, its decoupled design retains compatibility with existing watermarking techniques. This satisfies the need of model owners to actively control model ownership while preserving the traceability capabilities provided by traditional watermarking approaches.Extensive experimental evaluations confirm a strong dependency between credential verification and image quality across various attack scenarios. Moreover, when combined with typical post-processing operations, PCDIFF demonstrates powerful performance alongside conventional watermarking methods. This work shifts the paradigm from passive detection to proactive enforcement of authorization, laying the groundwork for IP management of diffusion models.

Problem

• Protects intellectual property of diffusion models
• Controls image generation quality via authentication
• Maintains compatibility with existing watermarking techniques

Innovation

• Proactive access control framework for diffusion models
• Trainable fuser module with hierarchical authentication layers
• Decoupled design compatible with existing watermarking techniques