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

• URL: http://arxiv.org/abs/2605.29809v1
• Date: Thu, 28 May 2026 11:54:52 GMT
• Title: Cert-LAS: Toward Certified Model Ownership Verification for Text-to-Image Diffusion Models via Layer-Adaptive Smoothing
• Abstract summary: Large-scale text-to-image (T2I) diffusion models have enabled unprecedented creative applications, but their unauthorized use has raised serious intellectual property concerns.<n>We propose Cert-LAS, the first certified MOV method for T2I models based on layer-adaptive smoothing.<n>Under certain conditions, our Cert-LAS can still achieve reliable verification even in the presence of malicious removal attacks.
• Score: 75.56230265544042
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• GoodDiffusion: Proactive Copyright Protection for Diffusion Bridge Models via Learnable Sample-specific Signatures [88.63093209858387]
  We propose GoodDiffusion, inspired by backdoor mechanisms, to enforce model-level use-time control.<n>GoodDiffusion preserves high-quality generation for authorized queries carrying valid signatures, yet refuses to generate for unauthorized inputs.<n>To strengthen security, we introduce a Learnable Signature Network (LSN) that assigns sample-specific signatures conditioned on each input.
  arXiv  Detail & Related papers  (2026-06-29T04:07:09Z)
• SIF: Semantically In-Distribution Fingerprints for Large Vision-Language Models [18.815997579213317]
  Existing ownership verification methods rely on semantically abnormal queries or out-of-distribution responses as fingerprints.<n>We propose SIF (Semantically In-Distribution Fingerprints), a non-intrusive ownership verification framework that requires no parameter modification.<n>SIF achieves strong stealthiness and robustness, providing a practical solution for LVLM copyright protection.
  arXiv  Detail & Related papers  (2026-04-18T15:52:42Z)
• SEAL: Subspace-Anchored Watermarks for LLM Ownership [12.022506016268112]
  We propose SEAL, a subspace-anchored watermarking framework for large language models.<n> SEAL embeds multi-bit signatures directly into the model's latent representational space, supporting both white-box and black-box verification scenarios.<n>We conduct comprehensive experiments on multiple benchmark datasets and six prominent LLMs to demonstrate SEAL's superior effectiveness, fidelity, efficiency, and robustness.
  arXiv  Detail & Related papers  (2025-11-14T14:44:11Z)
• SWAP: Towards Copyright Auditing of Soft Prompts via Sequential Watermarking [58.475471437150674]
  We propose sequential watermarking for soft prompts (SWAP)<n>SWAP encodes watermarks through a specific order of defender-specified out-of-distribution classes.<n>Experiments on 11 datasets demonstrate SWAP's effectiveness, harmlessness, and robustness against potential adaptive attacks.
  arXiv  Detail & Related papers  (2025-11-05T13:48:48Z)
• CertDW: Towards Certified Dataset Ownership Verification via Conformal Prediction [48.82467166657901]
  We propose the first certified dataset watermark (i.e., CertDW) and CertDW-based certified dataset ownership verification method.<n>Inspired by conformal prediction, we introduce two statistical measures, including principal probability (PP) and watermark robustness (WR)<n>We prove there exists a provable lower bound between PP and WR, enabling ownership verification when a suspicious model's WR value significantly exceeds the PP values of benign models trained on watermark-free datasets.
  arXiv  Detail & Related papers  (2025-06-16T07:17:23Z)
• Towards Dataset Copyright Evasion Attack against Personalized Text-to-Image Diffusion Models [52.877452505561706]
  We propose the first copyright evasion attack specifically designed to undermine dataset ownership verification (DOV)<n>Our CEAT2I comprises three stages: watermarked sample detection, trigger identification, and efficient watermark mitigation.<n>Our experiments show that our CEAT2I effectively evades DOV mechanisms while preserving model performance.
  arXiv  Detail & Related papers  (2025-05-05T17:51:55Z)
• Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning [58.57194301645823]
  Large language models (LLMs) are increasingly integrated into real-world personalized applications.<n>The valuable and often proprietary nature of the knowledge bases used in RAG introduces the risk of unauthorized usage by adversaries.<n>Existing methods that can be generalized as watermarking techniques to protect these knowledge bases typically involve poisoning or backdoor attacks.<n>We propose name for harmless' copyright protection of knowledge bases.
  arXiv  Detail & Related papers  (2025-02-10T09:15:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.