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arxiv: 2606.29759 · v1 · pith:MNMQYQ4Dnew · submitted 2026-06-29 · 💻 cs.CR

GoodDiffusion: Proactive Copyright Protection for Diffusion Bridge Models via Learnable Sample-specific Signatures

Shixi Qin , Zhiyong Yang , Shilong Bao , Zitai Wang , Qianqian Xu , Qingming Huang This is my paper

Pith reviewed 2026-06-30 05:55 UTC · model grok-4.3

classification 💻 cs.CR
keywords copyright protectiondiffusion modelsproactive defenselearnable signaturesbackdoor mechanismsgenerative model securitysample-specific signatures
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The pith

GoodDiffusion trains diffusion bridge models to generate high-quality outputs only for inputs carrying valid sample-specific signatures while refusing unauthorized queries.

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

The paper introduces GoodDiffusion to provide proactive copyright protection by internalizing authorization directly into the diffusion generation process. It shows that conventional static signatures are fragile because surrogates can be recovered efficiently through gradient-based optimization on the model. To address this, the authors propose a Learnable Signature Network that produces input-conditioned signatures, breaking their universality so that recovered surrogates do not transfer across samples. Experiments confirm that authorized inputs retain generation quality while unauthorized ones trigger refusal. This shifts protection from post-hoc detection to built-in use-time control.

Core claim

GoodDiffusion achieves selective generation by training the diffusion bridge model to respond correctly only when the input is paired with a valid signature from the Learnable Signature Network; unauthorized inputs without matching signatures cause the model to refuse generation. The paper proves that static signatures allow efficient surrogate recovery and demonstrates that sample-specific conditioning prevents transfer of any recovered surrogate to new inputs.

What carries the argument

Learnable Signature Network (LSN) that generates input-conditioned sample-specific signatures to enforce selective permissiveness in the generative process.

If this is right

  • Authorized queries with valid signatures receive full-quality generation from the protected model.
  • Unauthorized inputs trigger refusal rather than degraded or full output.
  • Static signature designs are shown to be vulnerable because surrogates can be recovered by gradient optimization.
  • Sample-specific signatures from the LSN prevent any recovered surrogate from working on different inputs.

Where Pith is reading between the lines

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

  • The approach could be tested on other generative architectures by replacing the diffusion bridge with alternative backbones while keeping the LSN structure.
  • If the LSN parameters must remain secret, deployment would require secure model distribution channels beyond standard open release.
  • Refusal behavior might be extended to produce detectable artifacts instead of outright blocking, allowing attribution even on attempted unauthorized use.

Load-bearing premise

The Learnable Signature Network can be trained so that its signatures remain hard to surrogate without internal model access and that adding the refusal behavior does not degrade quality on authorized inputs.

What would settle it

An attacker without access to model internals recovers a surrogate signature via optimization that produces high-quality outputs on new unauthorized inputs, or authorized generation quality drops noticeably after training.

Figures

Figures reproduced from arXiv: 2606.29759 by Qianqian Xu, Qingming Huang, Shilong Bao, Shixi Qin, Zhiyong Yang, Zitai Wang.

Figure 1
Figure 1. Figure 1: Overview of GoodDiffusion. (Left) The static signature design is vulnerable in white-box scenarios, while the sample-specific signature design can effectively defend against malicious model thieves. (Right) The trained DBM integrates two diffusion bridges. The authorized trajectory (blue) follows pauth(x˜1)→pauth(x0 | x˜1). The unauthorized trajectory (red) follows pwarn(x1)→pwarn(x¯0). Although watermarks… view at source ↗
Figure 2
Figure 2. Figure 2: Visualization of GoodDiffusion outputs. We show the results on CelebA for three I2I tasks: super-resolution, inpainting, and deblurring. The results demonstrate that GoodDiffusion proactively prevents unauthorized usages by producing the predefined warning image, while generating high-quality target images with sample-specific signatures [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: ImageNet: Average generation quality across three I2I tasks. It is obvious that GoodDiffusion generates high-quality and similar-to-normal outputs with authorized inputs. Otherwise, the generation quality becomes significantly worse. We simulate the white-box adversary introduced in Sec. 4.4 to recover surrogate signatures for both signature schemes. For the static signature, the recovered surrogate signat… view at source ↗
Figure 4
Figure 4. Figure 4: Visualization of Signatures and Surrogate Perturba￾tions. The adversary successfully recovered the Gaussian noise pattern used in the bottom right corner of the static signature, but failed to recover a surrogate for the sample-specific signature. cial for safeguarding the rights of model owners. While the watermarking methods provide a basic level of protection, the passive nature of these methods cannot … view at source ↗
Figure 5
Figure 5. Figure 5: Additional Visualization Results on CelebA Super-Resolution. We present more visualization results of GoodDiffusion on the CelebA Super-Resolution task with different diffusion bridge models. Top 2 rows: DDBM-VP. 3rd and 4th rows: DDBM-VE. 5th and 6th rows: I2SB. Bottom 2 rows: DBIM. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Additional Visualization Results on CelebA Inpainting. We present more visualization results of GoodDiffusion on the CelebA Inpainting task with different diffusion bridge models. Top 2 rows: DDBM-VP. 3rd and 4th rows: DDBM-VE. 5th and 6th rows: I2SB. Bottom 2 rows: DBIM. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Additional Visualization Results on CelebA Deblurring. We present more visualization results of GoodDiffusion on the CelebA Deblurring task with different diffusion bridge models. Top 2 rows: DDBM-VP. 3rd and 4th rows: DDBM-VE. 5th and 6th rows: I2SB. Bottom 2 rows: DBIM. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Additional Visualization Results on ImageNet Super-Resolution. We present more visualization results of GoodDiffusion on the ImageNet Super-Resolution task with different diffusion bridge models. Top 2 rows: DDBM-VP. 3rd and 4th rows: DDBM-VE. 5th and 6th rows: I2SB. Bottom 2 rows: DBIM. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Additional Visualization Results on ImageNet Inpainting. We present more visualization results of GoodDiffusion on the ImageNet Inpainting task with different diffusion bridge models. Top 2 rows: DDBM-VP. 3rd and 4th rows: DDBM-VE. 5th and 6th rows: I2SB. Bottom 2 rows: DBIM. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Additional Visualization Results on ImageNet Deblurring. We present more visualization results of GoodDiffusion on the ImageNet Deblurring task with different diffusion bridge models. Top 2 rows: DDBM-VP. 3rd and 4th rows: DDBM-VE. 5th and 6th rows: I2SB. Bottom 2 rows: DBIM. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Visualization Results for Security Analysis: Static Signature Setting. We implement the static signature setting on the CelebA dataset with the I2SB bridge model. Top 2 rows: Super-Resolution task. Middle 2 rows: Inpainting task. Bottom 2 rows: Deblurring task. The results show that the adversary can recover a static surrogate signature that is similar to the true static signature. The surrogate signature… view at source ↗
Figure 12
Figure 12. Figure 12: Visualization Results for Security Analysis: Sample-Specific Signature Setting. We implement the sample-specific signature setting on the CelebA dataset with the I2SB bridge model. Top 2 rows: Super-Resolution task. Middle 2 rows: Inpainting task. Bottom 2 rows: Deblurring task. The results show that the adversary fails to recover a universal surrogate signature. The generation results for unauthorized in… view at source ↗
read the original abstract

This paper tackles the challenging problem of developing a proactive copyright protection mechanism that cuts off unauthorized use of diffusion bridge models. Existing studies largely fall into post-hoc attribution (e.g., watermarking and fingerprinting) or degradation-only defenses, which offer only indirect and limited preventive effects. We therefore propose GoodDiffusion, inspired by backdoor mechanisms, to enforce model-level use-time control by internalizing authorization into the generative process through a selectively permissive, otherwise closed behavior. Specifically, GoodDiffusion preserves high-quality generation for authorized queries carrying valid signatures, yet refuses to generate for unauthorized inputs. We further theoretically show that naive static-signature designs (like conventional backdoor injection) are fundamentally fragile, since a surrogate signature can be efficiently recovered via gradient-based optimization. To strengthen security, we introduce a Learnable Signature Network (LSN) that assigns sample-specific signatures conditioned on each input. This breaks the universality of signatures and prevents a surrogate from transferring across inputs. Extensive experiments validate that GoodDiffusion effectively blocks unauthorized use while maintaining strong generation quality for authorized users.

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.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes GoodDiffusion, a proactive copyright protection mechanism for diffusion bridge models. It embeds authorization into the generative process via a Learnable Signature Network (LSN) that produces input-conditioned, sample-specific signatures. Authorized queries with valid signatures yield high-quality outputs while unauthorized inputs trigger refusal. The paper theoretically shows that static signatures are fragile under gradient-based surrogate recovery and that LSN breaks signature universality to prevent transfer. Extensive experiments are claimed to confirm effective blocking of unauthorized use alongside preserved generation quality for authorized users.

Significance. If the theoretical analysis and experimental validation hold, the work advances proactive model-level IP controls beyond post-hoc attribution or degradation defenses. The explicit demonstration of static-signature fragility and the LSN design to condition signatures on inputs address a core vulnerability in backdoor-style protections. Credit is due for the claimed theoretical fragility result and the extensive experiments that reportedly separate security from authorized performance.

major comments (2)
  1. [Theoretical analysis] Theoretical analysis section: the fragility claim for static signatures (gradient recovery of a surrogate) is load-bearing for motivating LSN, yet the manuscript must specify the exact optimization objective, threat-model assumptions (white-box gradient access?), and recovery efficiency metric to allow verification.
  2. [LSN design] LSN design and security argument: the claim that input-conditioned signatures prevent surrogate transfer across inputs requires either a formal bound or a concrete surrogate-attack experiment (success rate, transfer accuracy) to substantiate that universality is broken; this is central to the security contribution.
minor comments (2)
  1. [Abstract] Abstract and introduction: the term 'diffusion bridge models' is used without a brief definition or citation; add one sentence or reference for accessibility.
  2. [Experiments] Experimental tables: ensure all reported metrics (FID, CLIP score, refusal rate) include standard deviations and direct comparison to the unmodified baseline model to quantify any authorized-performance impact.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and positive assessment of the work's significance. We address each major comment below and will revise the manuscript accordingly to strengthen clarity and evidence.

read point-by-point responses

  1. Referee: [Theoretical analysis] Theoretical analysis section: the fragility claim for static signatures (gradient recovery of a surrogate) is load-bearing for motivating LSN, yet the manuscript must specify the exact optimization objective, threat-model assumptions (white-box gradient access?), and recovery efficiency metric to allow verification.

    Authors: We agree that additional specification is needed for verifiability. The revised manuscript will expand the theoretical analysis to explicitly define the optimization objective as minimizing the discrepancy (e.g., L2 norm on generated latents) between outputs using the recovered surrogate signature and the true signature, state the threat model as white-box gradient access to the diffusion bridge model, and define the recovery efficiency metric as the number of gradient descent iterations required to reach a generation quality threshold of at least 90% of authorized performance. These details will be added to allow direct verification of the fragility result. revision: yes

  2. Referee: [LSN design] LSN design and security argument: the claim that input-conditioned signatures prevent surrogate transfer across inputs requires either a formal bound or a concrete surrogate-attack experiment (success rate, transfer accuracy) to substantiate that universality is broken; this is central to the security contribution.

    Authors: We acknowledge that while the manuscript motivates the LSN via the breaking of signature universality and reports broad experimental validation of blocking performance, a targeted quantification of transfer would provide stronger substantiation. In the revision, we will add a dedicated surrogate transfer experiment reporting success rates and transfer accuracy (defined as the fraction of cross-input surrogate applications that produce high-quality unauthorized outputs), demonstrating empirically that input-conditioned signatures prevent effective transfer. If a concise formal bound can be derived without lengthening the paper excessively, we will include that as well. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper's central claims rest on a theoretical demonstration that static signatures are fragile under gradient recovery and the introduction of an LSN to produce input-conditioned signatures that break universality. No equations, self-citations, or fitted parameters are shown in the abstract that reduce any prediction or uniqueness result to the inputs by construction. The argument structure is presented as independent, with experiments offered as external validation. This is the common case of a self-contained proposal without load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only abstract available; cannot identify specific free parameters, axioms or invented entities from the provided text.

pith-pipeline@v0.9.1-grok · 5726 in / 967 out tokens · 23225 ms · 2026-06-30T05:55:45.616919+00:00 · methodology

discussion (0)

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Reference graph

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    2014