Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models | Paper Citation Record

Observation 0a709215-8512-4f9a-bd1b-2112cd1b60a1 · outbound

This paper cites Imperceptible protection against style imitation from diffusion models.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Imperceptible protection against style imitation from diffusion models

Reference 1

Resolution: verified exact; raw_fallback, observed 2026-08-11T15:01:39.812186Z

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Observation 41656da2-22bf-4f2f-b6d7-f75dc57ce72d · outbound

This paper cites Dream- styler: Paint by style inversion with text-to-image diffusion models.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Dream- styler: Paint by style inversion with text-to-image diffusion models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.480291Z

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Observation 2f4ed36f-978f-489e-9c92-34b95d0ca0de · outbound

This paper cites k-means++: The advantages of careful seeding.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models k-means++: The advantages of careful seeding

Reference 3

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Observation a5c34591-90f1-45c5-b988-20654022a2c6 · outbound

This paper cites Vggface2: A dataset for recognising faces across pose and age.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Vggface2: A dataset for recognising faces across pose and age

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.451489Z

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Observation 8ada736b-7c34-41e8-9a30-ad5062dfcdd6 · outbound

This paper cites Universal Adversarial Perturbations: A Survey.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Universal Adversarial Perturbations: A Survey

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:01:39.715715Z

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Observation bcb5d85f-94a1-4f02-9c4b-336760ecefd2 · outbound

This paper cites Topiq: A top-down approach from semantics to distortions for image quality assessment.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Topiq: A top-down approach from semantics to distortions for image quality assessment

Reference 6

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Observation 41e04d99-7ed4-4993-8c9d-eff746e73ee8 · outbound

This paper cites Sparse and imperceiv- able adversarial attacks.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Sparse and imperceiv- able adversarial attacks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.420678Z

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Observation 44c672ae-0288-4485-970c-86b5b82f8237 · outbound

This paper cites Saliency attack: Towards imperceptible black-box adversarial attack.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Saliency attack: Towards imperceptible black-box adversarial attack

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.396216Z

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Observation 79f9c0c2-0a44-4b8a-bbfe-876088b560b1 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Imagenet: A large-scale hierarchical image database

Reference 9

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Observation 18929871-6fd5-45d0-8024-aecf35e26d0a · outbound

This paper cites Image quality assessment: Unifying structure and texture similarity.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Image quality assessment: Unifying structure and texture similarity

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.367901Z

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Observation 030ff06e-f78c-4c10-9a48-b737811c1d18 · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:38.868130Z

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Observation 4d22eb72-6c95-42ad-afee-9e14311c6669 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Explaining and Harnessing Adversarial Examples

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:38.874095Z

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Observation 29f453e5-2265-49b2-8c86-c8b1a7a4ef7e · outbound

This paper cites Low Frequency Adversarial Perturbation.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Low Frequency Adversarial Perturbation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:38.879627Z

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Observation 8befb83b-c73d-426a-8f80-8265a6beecf8 · outbound

This paper cites Learning universal adver- sarial perturbations with generative models.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Learning universal adver- sarial perturbations with generative models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.350927Z

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Observation ed4703d3-d4b2-44e9-aa20-05c3575eed30 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.335420Z

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Observation 680f742b-8653-4613-81f5-fa03d40186fd · outbound

This paper cites Denoising dif- fusion probabilistic models.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Denoising dif- fusion probabilistic models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:38.895639Z

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Observation 091c387a-5493-46b9-8075-8c88d805e8b4 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:38.902184Z

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Observation 4ae5a40f-1f77-43b1-a59b-66638dc5c1f4 · outbound

This paper cites Composer: Creative and Controllable Image Synthesis with Composable Conditions.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Composer: Creative and Controllable Image Synthesis with Composable Conditions

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:38.907968Z

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Observation 18fac484-7ddb-4ab4-ad55-565e8853cadc · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models A style-based generator architecture for generative adversarial networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.306292Z

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Observation ca4455bf-c708-4378-8632-4ab8a94ff000 · outbound

This paper cites One millisecond face alignment with an ensemble of regression trees.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models One millisecond face alignment with an ensemble of regression trees

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.290603Z

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Observation a441a251-d389-4800-b5a8-756e788c96d9 · outbound

This paper cites DiffBlender: Composable and Versatile Multimodal Text-to-Image Diffusion Models.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models DiffBlender: Composable and Versatile Multimodal Text-to-Image Diffusion Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:38.922843Z

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Observation 8f4a4859-cbc5-489c-8ac9-a026c43005c9 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Adam: A Method for Stochastic Optimization

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:38.928204Z

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Observation 2f90b9d4-c98a-4d64-9f97-4dfb90658692 · outbound

This paper cites Imagenet classification with deep convolutional neural net- works.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Imagenet classification with deep convolutional neural net- works

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:38.935244Z

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Observation 2c9ff495-f86b-424d-8752-671f1cfbb4c9 · outbound

This paper cites Multi-concept customization of text-to-image diffusion.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Multi-concept customization of text-to-image diffusion

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.262891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fe288952-6c70-4aeb-89f2-7c8c649c997d · outbound

This paper cites Perceptual Adversarial Robustness: Defense Against Unseen Threat Models.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Perceptual Adversarial Robustness: Defense Against Unseen Threat Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:38.945376Z

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Observation 52ab2c75-8c05-4b9d-8588-209ea356a1d9 · outbound

This paper cites Attentions help cnns see better: Attention-based hybrid image quality 9 assessment network.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Attentions help cnns see better: Attention-based hybrid image quality 9 assessment network

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.246174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 85fdfb4c-2c5d-4a84-9c8b-fe0dd23460a3 · outbound

This paper cites Mist: Towards Improved Adversarial Examples for Diffusion Models.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Mist: Towards Improved Adversarial Examples for Diffusion Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:38.954746Z

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Observation 6617b810-5526-412b-808b-b3d300be17ae · outbound

This paper cites Adversarial example does good: preventing paint- ing imitation from diffusion models via adversarial exam- ples.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Adversarial example does good: preventing paint- ing imitation from diffusion models via adversarial exam- ples

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.228534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1d8d60e3-7ed4-4717-bb28-b609514baf0d · outbound

This paper cites Universal adversarial per- turbation via prior driven uncertainty approximation.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Universal adversarial per- turbation via prior driven uncertainty approximation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.211412Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 05ce0460-3290-4e76-8b61-fb1a7c4887d9 · outbound

This paper cites Metacloak: Preventing unauthorized subject-driven text-to-image diffusion-based synthesis via meta-learning.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Metacloak: Preventing unauthorized subject-driven text-to-image diffusion-based synthesis via meta-learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.193812Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4e1fa41d-3f9b-4844-975e-15cfdcfbef38 · outbound

This paper cites Towards imperceptible and robust adversarial example attacks against neural networks.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Towards imperceptible and robust adversarial example attacks against neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.175379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f78270a6-6bcb-4db4-87cd-b93d416674cb · outbound

This paper cites Frequency-driven imperceptible adversarial attack on semantic similarity.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Frequency-driven imperceptible adversarial attack on semantic similarity

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.158405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dca23c8d-1083-4903-8df3-c9a643b5ef4c · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 33

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Observation 7d740eee-2894-40da-b3c2-c4dbbd677a8e · outbound

This paper cites SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:38.989863Z

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Observation 3edbd935-6bea-42a3-851f-ef3cad20d85e · outbound

This paper cites Sparsefool: a few pixels make a big dif- ference.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Sparsefool: a few pixels make a big dif- ference

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.137823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1ca6a162-ce08-4ffa-a555-e19bfa135faf · outbound

This paper cites Universal adversarial perturba- tions.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Universal adversarial perturba- tions

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.121963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4daaddaa-e22a-4496-9971-760d82fc84fd · outbound

This paper cites Generalizable data-free objective for crafting univer- sal adversarial perturbations.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Generalizable data-free objective for crafting univer- sal adversarial perturbations

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.104585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 833894d5-f774-4b39-8938-40713e372a5b · outbound

This paper cites Nag: Network for adversary generation.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Nag: Network for adversary generation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.087041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 26e818fd-fa40-4ab1-b0f1-93154bdc5d22 · outbound

This paper cites Ask, acquire, and attack: Data-free uap generation using class impressions.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Ask, acquire, and attack: Data-free uap generation using class impressions

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.070245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e838bf9d-26f1-4ec3-8819-873f8addb4db · outbound

This paper cites T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:39.021012Z

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Observation ec67c55b-432d-47f3-84ce-a7aca2a61fc6 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:39.026662Z

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Observation 12f024c2-1171-4a4e-a52a-3de5f6726282 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Learning transferable visual models from natural language supervi- sion

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:39.035185Z

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Observation 2a4b1dab-9719-4b85-9afb-75344265eade · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models High-resolution image synthesis with latent diffusion models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.043749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 282cda7d-7714-44ab-b568-49da5d1e6c97 · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:40.026713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9e4ad765-23e5-4000-b4e1-4f400da48405 · outbound

This paper cites Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:39.053690Z

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Observation 62e2f7bc-c3b4-48ad-94bd-bbd581636273 · outbound

This paper cites Raising the Cost of Malicious AI-Powered Image Editing.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Raising the Cost of Malicious AI-Powered Image Editing

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:39.059495Z

Source-reported events for the cited work

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Observation b0e6176e-3aa1-487f-8186-ed8e10262947 · outbound

This paper cites Colorfool: Semantic adversarial coloriza- tion.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Colorfool: Semantic adversarial coloriza- tion

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:39.986478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d943e90b-ce50-48f5-9b57-9ca51efbd3e0 · outbound

This paper cites Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:39.076297Z

Source-reported events for the cited work

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Observation 05a16aa2-b0b2-469c-a1c6-b649e06cbfcd · outbound

This paper cites StyleDrop: Text-to-Image Generation in Any Style.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models StyleDrop: Text-to-Image Generation in Any Style

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:39.082531Z

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Unavailable: canonical work link unavailable.

Observation 611792a2-d75e-4dbb-a3e4-4fb4dbaccce9 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:39.087797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c61493c3-e694-4a81-890a-0a6d2893deb0 · outbound

This paper cites Anti-dreambooth: Pro- tecting users from personalized text-to-image synthesis.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Anti-dreambooth: Pro- tecting users from personalized text-to-image synthesis

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:39.967410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 97a9e17f-d127-436b-80cb-2a941f9978d8 · outbound

This paper cites Prompt-agnostic adversarial perturbation for customized dif- fusion models.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Prompt-agnostic adversarial perturbation for customized dif- fusion models

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:39.949735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e71c9b28-1d00-4574-8b8e-74ba5bd0014a · outbound

This paper cites Demiguise Attack: Crafting Invisible Semantic Adversarial Perturbations with Perceptual Similarity.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Demiguise Attack: Crafting Invisible Semantic Adversarial Perturbations with Perceptual Similarity

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:01:39.353995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dcc63c3d-f0e7-48b7-a06e-2a206c39f472 · outbound

This paper cites Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:39.110800Z

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This paper cites Perturbing attention gives you more bang for the buck: Subtle imaging perturbations that effi- ciently fool customized diffusion models.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Perturbing attention gives you more bang for the buck: Subtle imaging perturbations that effi- ciently fool customized diffusion models

Reference 55

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This paper cites Toward effective protection against diffusion-based mimicry through score distillation.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Toward effective protection against diffusion-based mimicry through score distillation

Reference 56

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Observation a86f94b1-c86d-471d-8d15-3c81de4484b2 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Adding conditional control to text-to-image diffusion models

Reference 57

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This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models The unreasonable effectiveness of deep features as a perceptual metric

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:39.883266Z

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This paper cites Towards large yet imperceptible adversarial image perturbations with perceptual color distance.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Towards large yet imperceptible adversarial image perturbations with perceptual color distance

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:39.866497Z

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This paper cites Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation

Reference 60

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This paper cites down 1”, “down 2.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models down 1”, “down 2

Reference 61

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Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Unresolved cited work

Reference 63

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Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models Unresolved cited work

Reference 2023

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This paper cites A paint- ing in <sks> style.

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models A paint- ing in <sks> style

Reference 5122

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