mlsanityData to 14 Oct 2026

Beyond User-Side Defenses: Image Protection in Open-Weight Diffusion Models

arXiv 2610.16835 ↗First posted 12 Oct 2026cs.CR cs.CVcited by 0

On the map: Diffusion models → Robustness, privacy and adversarial aspects

Abstract (arXiv)

Model providers can support image protection, but open-weight deployment introduces practical constraints. We propose four requirements: protection effectiveness, clean fidelity, compatibility, and tamper resistance. We incorporate long-horizon attack simulations to strengthen resistance to protection removal. Experiments with a single protection tool show improved resistance to re-alignment at a cost to clean fidelity, motivating further research toward practical model-side protection.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models. Builds on 26 of its 29 references that are in the map. First posted on arXiv 12 Oct 2026.

Builds on

26 of 29 references.

Closest ancestors · 14

Same direction, newest first

  1. 2025Perturb a Model, Not an Image: Towards Robust Privacy Protection via Anti-Personalized Diffusion Models3 Nov
  2. 2025GIFT: Gradient-aware Immunization of diffusion models against malicious Fine-Tuning with safe concepts retention18 Jul
  3. 2025GuardDoor: Safeguarding Against Malicious Diffusion Editing via Protective Backdoors5 Mar
  4. 2025CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models11 Feb
  5. 2024Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models16 Dec
  6. 2024SleeperMark: Towards Robust Watermark against Fine-Tuning Text-to-image Diffusion Models6 Dec
  7. 2024Imperceptible Protection against Style Imitation from Diffusion Models28 Mar
  8. 2023MetaCloak: Preventing Unauthorized Subject-driven Text-to-image Diffusion-based Synthesis via Meta-learning22 Nov
  9. 2023IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI30 Oct
  10. 2023Mist: Towards Improved Adversarial Examples for Diffusion Models22 May
  11. 2023Anti-DreamBooth: Protecting users from personalized text-to-image synthesis27 Mar
  12. 2023Raising the Cost of Malicious AI-Powered Image Editing13 Feb
2 more
  1. 2023Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples9 Feb
  2. 2022Diffusion Models for Adversarial Purification16 May

Other references · 12

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  4. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  5. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
  6. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,937
  7. 2024AnyEdit: Mastering Unified High-Quality Image Editing for Any Ideacited by 241
  8. 2023Glaze: Protecting Artists from Style Mimicry by Text-to-Image Modelscited by 205
  9. 2024Tamper-Resistant Safeguards for Open-Weight LLMscited by 138
  10. 2022Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Modelscited by 57
  11. 2023DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Modelscited by 27
  12. 2023IMMA: Immunizing text-to-image Models against Malicious Adaptationcited by 18

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  1. 2024Dual-Model Defense: Safeguarding Diffusion Models from Membership Inference Attacks through Disjoint Data Splitting
  2. 2024TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors
  3. 2023Data Forensics in Diffusion Models: A Systematic Analysis of Membership Privacy
  4. 2022Red-Teaming the Stable Diffusion Safety Filter
  5. 2024DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing
  6. 2024PID: Prompt-Independent Data Protection Against Latent Diffusion Models
  7. 2025Anti-Diffusion: Preventing Abuse of Modifications of Diffusion-Based Models
  8. 2026Purify Once, Edit Freely: Breaking Image Protections under Model Mismatch