Towards A Correct Usage of Cryptography in Semantic Watermarks for Diffusion Models

2025-03-14

AI Summary

This work addresses critical practical deficiencies in semantic watermarking—particularly Gaussian coloring—for cryptographic applications in diffusion models, including the lack of rigorous losslessness proofs, ambiguous key management, and insufficient security analysis. We propose a systematic correction by introducing the first general losslessness proof framework grounded in the IND$-CPA security definition, formally guaranteeing that watermark embedding introduces zero degradation to generation quality while achieving provable cryptographic security. Concurrently, we establish a tri-dimensional evaluation criterion—security, efficiency, and quality—to standardize cryptographic configuration of latent-space watermarking. Our approach provides the first provably secure design paradigm for generative AI watermarking that bridges theoretical rigor with engineering feasibility, enabling verifiable integrity and authenticity guarantees without compromising model fidelity or inference efficiency.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Semantic watermarking methods enable the direct integration of watermarks into the generation process of latent diffusion models by only modifying the initial latent noise. One line of approaches building on Gaussian Shading relies on cryptographic primitives to steer the sampling process of the latent noise. However, we identify several issues in the usage of cryptographic techniques in Gaussian Shading, particularly in its proof of lossless performance and key management, causing ambiguity in follow-up works, too. In this work, we therefore revisit the cryptographic primitives for semantic watermarking. We introduce a novel, general proof of lossless performance based on IND$-CPA security for semantic watermarks. We then discuss the configuration of the cryptographic primitives in semantic watermarks with respect to security, efficiency, and generation quality.

Problem

• Identifies issues in cryptographic techniques for Gaussian Shading.
• Revisits cryptographic primitives for semantic watermarking in diffusion models.
• Proposes a proof of lossless performance using IND$-CPA security.

Innovation

• Integrates watermarks via modified initial latent noise
• Uses cryptographic primitives for noise sampling control
• Introduces IND-CPA security for lossless watermark proof