TrajPrint Enables Lossless Black-Box Verification of Diffusion Model Copyright

Global: TrajPrint Enables Lossless Black-Box Verification of Diffusion Model Copyright

Researchers have introduced a new framework called TrajPrint that allows lossless verification of diffusion model copyright in black‑box API settings. The work, posted on arXiv in January 2026, aims to protect high‑value intellectual property without degrading model performance. By extracting unique manifold fingerprints generated during deterministic sampling, the method can confirm whether a protected model produced a given watermarked image while rejecting non‑target models.

Background on Diffusion Model IP Challenges

Diffusion models have demonstrated exceptional generative capabilities, making them attractive assets for commercial and academic use. Existing protection strategies typically embed watermarks directly into model weights, which can impair generation quality, or rely on fingerprint extraction that requires access to the model’s internal denoising steps—an approach incompatible with many deployed black‑box services.

Core Principle of TrajPrint

TrajPrint operates by selecting a watermarked image as an anchor and precisely tracing the generation trajectory back to its origin point on the model’s latent manifold. This reverse tracing locks a distinctive fingerprint to the specific path taken, creating a verifiable link between the image and the model that produced it.

Dual‑End Anchoring and Fingerprint Synthesis

To strengthen robustness, the framework employs a joint optimization that introduces dual‑end anchoring. By synthesizing fingerprint noise that conforms strictly to the target manifold, the method ensures that the recovered watermark aligns with the intended model even after minor modifications or noise injection.

Verification Process

Verification proceeds through atomic inference, where the protected model is queried to reconstruct the watermarked image. Statistical hypothesis testing then assesses whether the observed reconstruction matches the expected fingerprint, providing a formal decision on model ownership.

Experimental Findings

Extensive experiments reported in the paper show that TrajPrint achieves lossless verification across a range of black‑box APIs. The approach demonstrates superior resilience to common model alterations, such as fine‑tuning or pruning, while maintaining the original generation quality of the diffusion model.

Implications and Future Work

The authors suggest that TrajPrint could serve as a practical tool for intellectual property enforcement in the rapidly expanding generative AI market. Ongoing research may explore extending the technique to other generative architectures and integrating it with legal frameworks for digital content protection.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.