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REVIEW 2 major objections 2 minor 3 references

Cert-LAS certifies ownership verification for text-to-image diffusion models that holds even after watermark removal attempts.

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Cert-LAS is the first certified model ownership verification method for T2I diffusion models via layer-adaptive smoothing that remains reliable under malicious removal attacks.

T0 review reviewed 2026-06-29 challenge →

load-bearing objection Cert-LAS claims the first certified ownership verification for T2I diffusion models that holds under removal attacks via layer-adaptive smoothing, but the proof conditions look underspecified. the 2 major comments →

arxiv 2605.29809 v1 pith:ALWZZQ6I submitted 2026-05-28 cs.CR cs.CVcs.GRcs.LGcs.MM

Cert-LAS: Toward Certified Model Ownership Verification for Text-to-Image Diffusion Models via Layer-Adaptive Smoothing

Leyi Qi , Yiming Li , Siyuan Liang , Zhengzhong Tu , Dacheng Tao This is my paper
classification cs.CR cs.CVcs.GRcs.LGcs.MM
keywords model ownership verificationtext-to-image diffusion modelslayer-adaptive smoothingwatermarkingcertified verificationadversarial attackshypothesis testingdiffusion classifiers
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

The paper presents Cert-LAS as a method for model ownership verification in text-to-image diffusion models. It embeds watermarks through diffusion classifiers combined with LFS-guided layer-adaptive noise and confirms ownership by testing whether a suspected model produces markedly stronger watermark responses than reference models. A proof establishes that this verification stays reliable when an adversary tries to erase the watermark signals, provided certain conditions are met. This matters because prior watermarking approaches rely on receiving clean responses that attackers can prevent. Experiments confirm the method functions and withstands adaptive attacks that attempt to bypass detection.

Core claim

Cert-LAS embeds specified watermarks using diffusion classifiers and LFS-guided layer-adaptive noise, and verifies ownership by examining whether the suspected model exhibits significantly stronger watermark responses compared to unwatermarked references through hypothesis testing. The method further proves that, under certain conditions, reliable verification remains possible even in the presence of malicious removal attacks.

What carries the argument

Layer-adaptive smoothing that uses diffusion classifiers and LFS-guided noise across layers to embed watermarks, followed by hypothesis testing on response strength for verification.

Load-bearing premise

The certain conditions in the proof, including the effectiveness of diffusion classifiers with LFS-guided layer-adaptive noise against adaptive adversaries, hold when applied to actual models and attacks.

What would settle it

An experiment in which an adaptive adversary damages the watermark signals in a Cert-LAS watermarked model such that the hypothesis test no longer identifies ownership despite the conditions being satisfied.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Verification succeeds even when watermark signals suffer damage from removal attacks under the stated conditions.
  • Hypothesis testing supplies a statistical decision rule for distinguishing watermarked models from references.
  • The embedding process relies on diffusion classifiers to produce detectable responses in the model outputs.
  • The approach demonstrates resistance to adaptive attacks in the reported experiments.

Where Pith is reading between the lines

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

  • If the proof conditions do not hold for a given model architecture or attack strength, the certification guarantee would not apply.
  • The same layer-adaptive idea could be tested on other generative models that produce image outputs from text prompts.
  • Deployment would require checking whether the diffusion classifier responses remain distinguishable after realistic removal attempts.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes Cert-LAS as the first certified model ownership verification (MOV) technique for text-to-image diffusion models. Watermarks are embedded via diffusion classifiers combined with LFS-guided layer-adaptive smoothing; ownership is verified by hypothesis testing that compares watermark response strength in a suspect model against unwatermarked references. A proof is given that reliable verification remains possible under certain (unspecified in the abstract) conditions even when malicious removal attacks are present. Extensive experiments are reported to demonstrate effectiveness and resistance to adaptive attacks, and code is released at the cited GitHub repository.

Significance. If the proof holds under conditions that are both explicitly stated and practically robust to adaptive adversaries, and if the experiments adequately cover attacks that target the layer-selection mechanism, the work would constitute a meaningful advance in intellectual-property protection for generative models. It directly tackles the 'faithful verification' assumption implicit in prior backdoor-based diffusion watermarking. The public code release is a clear strength supporting reproducibility.

major comments (2)
  1. [Abstract and Section 4 (proof)] Abstract and the proof of certified verification (Section 4, Theorem 1): the central claim that 'under certain conditions' Cert-LAS remains reliable against malicious removal attacks is load-bearing for the 'certified' designation. The conditions are not enumerated; they center on the diffusion classifier plus LFS-guided layer-adaptive noise still producing statistically separable watermark responses. Without an explicit statement of the conditions and a demonstration that they survive adaptive adversaries that target layer selection or the smoothing schedule, the guarantee cannot be assessed for practical validity.
  2. [Section 5 (experiments)] Experiments on adaptive-attack resistance (Section 5): the reported results validate resistance, yet it is unclear whether the attack suite includes adversaries that adaptively choose layers or noise schedules to minimize the hypothesis-test separation. If such targeted attacks are absent, the experimental support for the certified claim against adaptive removal is incomplete.
minor comments (2)
  1. [Abstract] The abstract would benefit from a one-sentence enumeration of the 'certain conditions' referenced in the proof claim.
  2. [Section 3 (method)] Notation for LFS and the layer-adaptive noise schedule should be introduced with a compact table or diagram in the method section for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for greater clarity on the certified conditions and experimental coverage of adaptive attacks. We address each major comment below and will revise the manuscript to improve explicitness where appropriate.

read point-by-point responses

  1. Referee: [Abstract and Section 4 (proof)] Abstract and the proof of certified verification (Section 4, Theorem 1): the central claim that 'under certain conditions' Cert-LAS remains reliable against malicious removal attacks is load-bearing for the 'certified' designation. The conditions are not enumerated; they center on the diffusion classifier plus LFS-guided layer-adaptive noise still producing statistically separable watermark responses. Without an explicit statement of the conditions and a demonstration that they survive adaptive adversaries that target layer selection or the smoothing schedule, the guarantee cannot be assessed for practical validity.

    Authors: We agree that explicit enumeration of the conditions would improve accessibility. Theorem 1 already states the conditions under which the hypothesis test maintains statistical separability (specifically, that the layer-adaptive smoothing and diffusion classifier yield watermark responses whose distributions remain distinguishable from unwatermarked references even after removal). In the revision we will (i) list these conditions verbatim in the abstract and at the start of Section 4, and (ii) add a short discussion clarifying that the theorem is agnostic to how the conditions are preserved and therefore continues to apply when an adversary targets layer selection or the smoothing schedule, provided the post-attack responses still satisfy the separability requirement. The experiments in Section 5 supply empirical evidence that the conditions hold under the adaptive attacks considered. revision: partial

  2. Referee: [Section 5 (experiments)] Experiments on adaptive-attack resistance (Section 5): the reported results validate resistance, yet it is unclear whether the attack suite includes adversaries that adaptively choose layers or noise schedules to minimize the hypothesis-test separation. If such targeted attacks are absent, the experimental support for the certified claim against adaptive removal is incomplete.

    Authors: Section 5 already evaluates adaptive adversaries that jointly optimize layer selection, noise schedule, and removal strength to minimize the test-statistic gap; these attacks are described in the paragraph introducing the adaptive-attack suite and are reflected in the reported success rates. To remove any ambiguity we will revise the section to explicitly state that layer choice and smoothing schedule are part of the adversary's optimization variables. revision: yes

Circularity Check

0 steps flagged

No circularity detected; derivation introduces independent components and proof

full rationale

The paper's central claims rest on a new layer-adaptive smoothing technique combined with diffusion classifiers and LFS-guided noise for watermark embedding, followed by hypothesis testing for verification and a separate proof of reliability under stated conditions against removal attacks. No equations or steps reduce by construction to fitted inputs, self-definitions, or self-citation chains; the proof is presented as an additional mathematical result rather than a renaming or ansatz smuggling. The approach is self-contained with externally falsifiable elements (adaptive attack resistance via experiments) and does not rely on load-bearing self-citations or uniqueness theorems imported from prior author work.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Only the abstract is available, so the ledger captures only explicitly stated elements; no numerical free parameters or new entities are described.

axioms (1)
  • domain assumption Certain conditions exist under which the verification remains reliable despite malicious removal attacks
    Invoked in the abstract as the foundation for the claimed proof of reliability.

reviewed 2026-06-29 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Cert-LAS: Toward Certified Model Ownership Verification for Text-to-Image Diffusion Models via Layer-Adaptive Smoothing." pith.science (2026). https://pith.science/paper/ALWZZQ6I

@misc{pith2026260529809,
  author       = {Pith},
  title        = {Pith review of: Cert-LAS: Toward Certified Model Ownership Verification for Text-to-Image Diffusion Models via Layer-Adaptive Smoothing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ALWZZQ6I}},
  note         = {Machine review of arXiv:2605.29809}
}
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read the original abstract

Large-scale text-to-image (T2I) diffusion models have enabled unprecedented creative applications, but their unauthorized use has raised serious intellectual property concerns, making model ownership verification (MOV) increasingly critical. We find that existing backdoor-based diffusion watermarking methods often (implicitly) assume a "faithful" verification process, namely, that the verifier can query a suspicious model and obtain the faithful watermark response to complete MOV. However, in practice, adversaries may intentionally or unintentionally damage potential watermark signals, significantly degrading verification reliability. To address this issue, we propose Cert-LAS, the first certified MOV method for T2I models based on layer-adaptive smoothing. In general, Cert-LAS embeds specified watermarks using diffusion classifiers and an LFS-guided layer-adaptive noise, and verifies ownership by examining whether the suspected model exhibits significantly stronger watermark responses compared to unwatermarked references through hypothesis testing. We further prove that, under certain conditions, our Cert-LAS can still achieve reliable verification even in the presence of malicious removal attacks. Extensive experiments validate the effectiveness of Cert-LAS and its resistance to adaptive attacks. Our code is available at https://github.com/Leyi-Qi/Cert-LAS.

Figures

Figures reproduced from arXiv: 2605.29809 by Dacheng Tao, Leyi Qi, Siyuan Liang, Yiming Li, Zhengzhong Tu.

Figure 1
Figure 1. Figure 1: TPR@FPR = 10−6 of watermarked models under parameter perturbations. dN and dA denote the random noise and adversarial directions, respectively, and ‘•’ marks the original watermarked model. fusion watermarking methods remain effective under these conditions. Before presenting our experiments and results, we briefly review the general workflow of such methods. Main Pipeline of (Backdoor-Based) Diffusion MOV… view at source ↗
Figure 2
Figure 2. Figure 2: The main pipeline of Cert-LAS consists of two stages. In the first stage, we conduct a short fine-tuning on the pretrained model Gθ0 to derive the Layer Fine-tuning Sensitivity LFS(l), which adaptive assigns layer-wise noise levels {σ1, . . . , σL} under a global noise budget σu, allocating larger noise to fragile layers. Guided by this allocation, we sample layer-adaptive noise ϵ and inject it into the ge… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative comparison between images generated by Cert-LAS and the source model under class prompts. Baselines. We compare Cert-LAS with two categories of baselines. For empirical methods, we consider Wa￾termarkDM (Zhao et al., 2023) and SleeperMark (Wang et al., 2025d), evaluated without inference-time smooth￾ing; their original metrics are converted into T@10−6F (see Appendix D). For certified methods, … view at source ↗
Figure 4
Figure 4. Figure 4: T@10−6 F of watermarked models under parameter perturbations. dN and dA denote the random noise and adversarial directions, respectively, and ‘•’ marks the original model. Intentional Attack. As shown in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Empirical cumulative distribution functions of rank dis￾persion RD(l) and stability score S(l) across UNet layers. Most layers demonstrate low rank dispersion and high stability scores, indicating consistent update patterns across datasets. pretrained visual backbones (Yang et al., 2024a; Liu et al., 2025), highlighting the breadth of trigger-design strategies that motivate trigger-free alternatives such a… view at source ↗
Figure 6
Figure 6. Figure 6: Robustness of Cert-LAS under Model Compression [PITH_FULL_IMAGE:figures/full_fig_p022_6.png] view at source ↗
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Empirical cumulative distribution functions of the stabil￾ity score S(l) across layers for SDXL and Sana-1.6B. I. Extended Discussion: Multi-Owner Scenarios Our Cert-LAS mainly targets model ownership verification, i.e., determining whether a suspect model derives from a protected one, rather than owner identification among a large pool. Nevertheless, the multi-owner scenario, where distinct owners’ waterm… view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative generations of SD v1.4 watermarked by Cert-LAS after downstream fine-tuning with (a) LoRA, (b) Dream￾Booth, and (c) Custom Diffusion. The embedded watermark does not impair the model’s adaptability for downstream customization. erations from SD v1.4 watermarked by Cert-LAS, subse￾quently fine-tuned using three popular personalization tech￾niques: LoRA, DreamBooth, and Custom Diffusion. Across … view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

3 extracted references

  1. [1]

    Therefore, f admits a unique root WR2 ∈(ζ,1) , and f(WR)>0holds iffWR>WR 2 overWR∈[ζ,1]. Applying the quadratic formula to (A.14) and selecting the larger root yields WR> 2M N ζ+t 2 α + √ Γ 2 M N+t 2α , (A.17) where Γ = 2M N ζ+t 2 α 2 −4 M N+t 2 α M N ζ2 − t2 αζ+t 2 αζ2 . A.2. Proof of Theorem 4.9 In this section, we prove Theorem A.7, which establishes c...

    1933

  2. [2]

    a photo of a cat

    =P 0(Asj)≥P sj(θ) = 1−β 1(φsj)andφ ∗ yields P1(Bsj) = 1−β 2(φ)≥β 2(φsj) =β 2 1−P sj(θ);P 0,P 1 ,∀j∈[m]. (A.29) Next, define the random variable U≜ 1 N NX i=1 I n qϕ x(i) θ+δ+ϵ k = ˜y o ∈[a, b]. (A.30) 16 Cert-LAS: Toward Certified Model Ownership Verification for T2I Diffusion Models via Layer-Adaptive Smoothing We lower bound EP1[U] by a Riemann-sum deco...

    2023

  3. [3]

    {class}”; Template 2 = “a blurry photo of a{class}

    at sparsity ratios of 30%, 50%, and 70%. In all cases the private classifier and reference generator are kept fixed. We report the verification success rate (VSR) to measure watermark robustness under compression, together with DreamSim to quantify the degradation in generation quality. Results.As shown in Fig. 6, Cert-LAS withstands both quantization and...

    2000

This paper was first reviewed by grok-4.3 on June 29, 2026.