NoisePrints: Distortion-Free Watermarks for Authorship in Private Diffusion Models

Abhinav Nakarmi; Eyal Ronen; Mahmood Sharif; Nir Goren; Oren Katzir; Or Patashnik

arxiv: 2510.13793 · v2 · submitted 2025-10-15 · 💻 cs.CV · cs.CR· cs.LG

NoisePrints: Distortion-Free Watermarks for Authorship in Private Diffusion Models

Nir Goren , Oren Katzir , Abhinav Nakarmi , Eyal Ronen , Mahmood Sharif , Or Patashnik This is my paper

Pith reviewed 2026-05-18 06:54 UTC · model grok-4.3

classification 💻 cs.CV cs.CRcs.LG

keywords diffusion modelswatermarkingauthorship verificationcopyright protectionzero-knowledge proofsimage generationvideo generationprivate models

0 comments

The pith

Random seeds from diffusion generation can verify authorship of images and videos without model access or output changes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes NoisePrints as a lightweight watermarking method for private diffusion models that treats the generation seed itself as the proof of authorship. The central observation is that the initial noise sampled from the seed correlates strongly with the final visual output. By folding a hash function into the noise sampling step, the scheme makes it computationally hard to recover a valid seed from a given image or to find an alternative seed that would pass verification. Verification then needs only the secret seed and the output content, with no requirement for model weights or heavy computation. The approach also supports zero-knowledge proofs to demonstrate ownership without exposing the seed and shows robustness under common manipulations.

Core claim

NoisePrints uses the diffusion model's initial noise seed, combined with a hash function in the sampling process, as a proof of authorship. The key property is that the noise is highly correlated with the generated content, enabling verification solely from the seed and output. Incorporating the hash makes recovering or forging a valid seed infeasible, and the method remains robust to various image manipulations. Ownership can be proven using zero-knowledge proofs without revealing the seed.

What carries the argument

The hashed seed used to initialize noise sampling, which ties the secret seed to the output content through correlation for later verification without changing the generation process.

If this is right

Third parties can verify authorship of generated images and videos using only the seed and output, without any model access.
The watermark introduces no visual distortion or change to the generated content.
Verification stays efficient and scalable even for state-of-the-art image and video diffusion models.
Robustness holds under common manipulations such as cropping, compression, or editing.
Zero-knowledge proofs allow owners to demonstrate possession of the seed without revealing it.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the noise-content correlation generalizes across additional generative architectures, the seed-based approach could extend beyond diffusion models.
Integration with timestamped ledgers might allow public registration of seeds to strengthen long-term ownership records.
Widespread adoption could reduce reliance on post-generation watermarking techniques that alter outputs.

Load-bearing premise

The initial noise derived from a seed is highly correlated with the generated visual content, allowing verification using only the seed and output without model access.

What would settle it

An experiment in which an output image verifies successfully against a different seed than the one used to generate it, or a practical recovery of the original seed from the output alone.

read the original abstract

With the rapid adoption of diffusion models for visual content generation, proving authorship and protecting copyright have become critical. This challenge is particularly important when model owners keep their models private and may be unwilling or unable to handle authorship issues, making third-party verification essential. A natural solution is to embed watermarks for later verification. However, existing methods require access to model weights and rely on computationally heavy procedures, rendering them impractical and non-scalable. To address these challenges, we propose NoisePrints, a lightweight watermarking scheme that utilizes the random seed used to initialize the diffusion process as a proof of authorship without modifying the generation process. Our key observation is that the initial noise derived from a seed is highly correlated with the generated visual content. By incorporating a hash function into the noise sampling process, we further ensure that recovering a valid seed from the content is infeasible. We also show that sampling an alternative seed that passes verification is infeasible, and demonstrate the robustness of our method under various manipulations. Finally, we show how to use cryptographic zero-knowledge proofs to prove ownership without revealing the seed. By keeping the seed secret, we increase the difficulty of watermark removal. In our experiments, we validate NoisePrints on multiple state-of-the-art diffusion models for images and videos, demonstrating efficient verification using only the seed and output, without requiring access to model weights.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper's claim of model-free seed verification via noise correlation looks hard to sustain once you account for how diffusion weights mediate the output.

read the letter

The main point on NoisePrints is that it wants to let a third party verify authorship of a diffusion-generated image or video using only the original seed and the final output, without ever touching the private model weights. If the correlation between seed-derived noise and content really holds up at scale, that would solve a practical problem for owners who keep models closed. The paper combines hashed noise sampling to block seed recovery, shows that forging a passing seed is hard, and adds zero-knowledge proofs so the owner can prove possession without revealing the seed. That combination is new enough to be worth noting, and it directly targets the scalability and access issues in prior watermarking work that needs model internals or heavy computation. The experiments on current image and video models plus the robustness checks under manipulations are a reasonable start for a first cut at the idea. Keeping the seed secret also raises the bar for removal attacks in a straightforward way. The soft spot is the load-bearing assumption that initial noise correlates strongly and reliably with the generated pixels. Diffusion runs many iterative denoising steps conditioned on the specific private weights, so any statistical dependence on the starting noise gets shaped by those weights. The abstract states the observation but gives no derivations, extractor details, or quantitative correlation numbers that would let you tell the true seed from alternatives after common post-processing. If that link turns out to be weak or model-specific, the efficient verification procedure cannot work as described. This is for people working on generative-model copyright and lightweight provenance schemes. A reader who needs ideas for private-model settings could pull useful pieces from the hashing and ZK parts even if the correlation evidence needs strengthening. I would send it to peer review because the problem is real and the approach is simple enough that referees can check the missing pieces without much overhead.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes NoisePrints, a lightweight watermarking scheme for authorship verification in private diffusion models. It treats the random seed used to initialize the diffusion noise as a proof of authorship, relying on the observation that this initial noise is highly correlated with the generated visual content. This enables efficient verification using only the seed and output image without requiring access to model weights. A hash function is incorporated into noise sampling to render seed recovery from content infeasible and to make sampling alternative passing seeds infeasible. Cryptographic zero-knowledge proofs are used to demonstrate ownership without revealing the seed. Experiments on state-of-the-art image and video diffusion models are reported to validate efficient verification and robustness under manipulations.

Significance. If the asserted correlation between initial noise and output holds with sufficient strength for reliable model-free verification, and if the cryptographic claims are substantiated, the work would offer a practical, distortion-free solution for third-party authorship attribution in private generative models. This addresses a real deployment barrier for existing watermarking methods that require model access or heavy computation. The integration of hashing and ZKPs for security and privacy is a positive design choice.

major comments (3)

[Abstract] Abstract: The central claim that 'the initial noise derived from a seed is highly correlated with the generated visual content' enabling verification without model weights is load-bearing, yet no quantitative support (e.g., correlation coefficients, verification success rates against random seeds, or extractor details) is provided. This leaves open whether any model-independent test reliably distinguishes the true seed after the full denoising process conditioned on private weights.
[Abstract] Abstract: The infeasibility statements ('recovering a valid seed from the content is infeasible' and 'sampling an alternative seed that passes verification is infeasible') rest on the hash function and standard cryptographic hardness but supply no concrete security reduction, parameter choices, or attack analysis. Without these, it is impossible to assess whether the hash-based sampling actually prevents forgery at the claimed scale.
[Experiments] Experiments section (implied by validation claims): Robustness 'under various manipulations' is asserted, but the manuscript provides no detailed metrics, tables, or ablation results quantifying verification accuracy after common post-processing or after the hash-augmented sampling. This is required to confirm that the correlation survives the manipulations while the security properties remain intact.

minor comments (2)

The abstract would be clearer if it briefly named the specific diffusion models and datasets used in the reported experiments.
Notation for the hash-augmented noise sampling process should be introduced with an equation or pseudocode early in the method description to aid readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thoughtful review and constructive suggestions. We address each of the major comments below and will revise the manuscript to incorporate additional details and clarifications as needed.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim that 'the initial noise derived from a seed is highly correlated with the generated visual content' enabling verification without model weights is load-bearing, yet no quantitative support (e.g., correlation coefficients, verification success rates against random seeds, or extractor details) is provided. This leaves open whether any model-independent test reliably distinguishes the true seed after the full denoising process conditioned on private weights.

Authors: The full manuscript includes experimental results on multiple diffusion models that demonstrate high verification accuracy using only the seed and output image, supporting the correlation claim. Specific quantitative metrics such as success rates against random seeds are reported in the Experiments section. To address the concern, we will include key quantitative results, including correlation insights and verification rates, directly in the abstract in the revised version. revision: yes
Referee: [Abstract] Abstract: The infeasibility statements ('recovering a valid seed from the content is infeasible' and 'sampling an alternative seed that passes verification is infeasible') rest on the hash function and standard cryptographic hardness but supply no concrete security reduction, parameter choices, or attack analysis. Without these, it is impossible to assess whether the hash-based sampling actually prevents forgery at the claimed scale.

Authors: The security properties are based on the one-way nature of the hash function used in noise sampling, making seed recovery computationally infeasible under standard cryptographic assumptions. We will add a dedicated security analysis subsection with parameter choices (e.g., hash output size) and a discussion of potential attacks to provide a more concrete foundation for these claims. revision: yes
Referee: [Experiments] Experiments section (implied by validation claims): Robustness 'under various manipulations' is asserted, but the manuscript provides no detailed metrics, tables, or ablation results quantifying verification accuracy after common post-processing or after the hash-augmented sampling. This is required to confirm that the correlation survives the manipulations while the security properties remain intact.

Authors: We agree that more detailed presentation is beneficial. The current manuscript validates robustness on state-of-the-art models, but we will expand the Experiments section with specific metrics, tables showing verification accuracy under manipulations such as cropping, compression, and noise addition, as well as ablations on the hash-augmented sampling. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on empirical observation and cryptographic assumptions

full rationale

The paper presents its core mechanism as relying on the stated key observation that initial noise from a seed correlates with generated content, combined with standard hash-based security and zero-knowledge proofs. No equations or derivations are shown that reduce a claimed result back to fitted parameters, self-definitions, or prior self-citations by construction. The verification procedure is described as model-free based on this correlation, without evidence of the correlation itself being defined circularly or statistically forced from the method's outputs. This is a normal self-contained proposal grounded in external assumptions rather than internal reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach depends on the domain assumption of strong noise-output correlation in diffusion processes and standard cryptographic hardness for hash functions and seed search; no free parameters or new entities are introduced in the abstract.

axioms (2)

domain assumption Initial noise derived from a seed is highly correlated with the generated visual content
Stated as the key observation enabling verification using only seed and output.
domain assumption Hash function makes recovering valid seed from content infeasible and alternative seed sampling infeasible
Relies on standard cryptographic assumptions about hash preimage resistance.

pith-pipeline@v0.9.0 · 5796 in / 1266 out tokens · 26279 ms · 2026-05-18T06:54:42.520589+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Our key observation is that the initial noise derived from a seed is highly correlated with the generated visual content... ϕ(x,s)≜⟨E(x),ε(h(s))⟩/(∥E(x)∥₂∥ε(h(s))∥₂)
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We show that sampling an alternative seed that passes verification is infeasible... Pr[ϕ≥τ]≤exp(−(d−1)τ²/2)

What do these tags mean?

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.