Pith. sign in

REVIEW 4 major objections 6 minor 64 references

Exploiting Watermark-Based Defense Mechanisms in Text-to-Image Diffusion Models for Unauthorized Data Usage

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read RATTAN shows that regenerating protected images through a diffusion model erases watermark-based defenses, dropping detection accuracy from 100% to 50% with as few as ten images.

desk verdict A credible adaptive attack on DIAGNOSIS whose own full-data ablation undermines the 'watermark eliminated' premise—worth reviewing, but needs a major revision. read the letter →

arxiv 2411.15367 v2 pith:WYQYARAB submitted 2024-11-22 cs.CV cs.AI

Soumil Datta , Shih-Chieh Dai , Leo Yu , Guanhong Tao This is my paper · ORCID
classification cs.CVcs.AI
keywords watermarkremovaltext-to-imagediffusionmodelsDIAGNOSIScontrolledimagegenerationfine-tuningunauthorizeddatausagemembershipinferenceintellectualpropertyprotection
verification ladder T0 review T1 audit T2 compute T3 formal

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 tries to establish that watermark-based protections on text-to-image diffusion models can be bypassed cheaply. It argues that watermarks live in low-level image details, while a model only needs high-level features to reproduce protected content. RATTAN regenerates a small set of protected images through an off-the-shelf diffusion model, guided by text, and fine-tunes the suspect model on those regenerated images. Across three datasets and 140 model instances, the method drops DIAGNOSIS detection from 100% to 50%, equivalent to random guessing, while preserving generation quality as measured by FID.

What carries the argument

The load-bearing mechanism is RATTAN's controlled image generation: add Gaussian noise to the protected input for a fraction of the full diffusion schedule, stopping at 60% of the noising process (gamma = 0.6), then denoise with an off-the-shelf text-guided Stable Diffusion model so coarse features survive while fine-grained watermark artifacts do not. The regenerated images are then used for LoRA fine-tuning of the watermarked model, which overwrites the learned watermark association while retaining content features.

What would settle it

Fine-tune a watermarked model using RATTAN outputs at the full 783-image scale and query DIAGNOSIS; the paper's own ablation already reports 20% detection at that scale, so if detection rises above chance as the cleaned set grows, the claim that RATTAN erases the watermark would fail.

Watch

Extended reading notes

Core claim

The central claim is that watermark-based defenses presume a model must be trained on the exact protected image content, but diffusion models can learn the same key features from a coarse regeneration that omits the watermark. RATTAN takes a watermarked image, partially noises it with a diffusion process, then denoises it with a frozen off-the-shelf Stable Diffusion model guided by the original text, producing an image that keeps structure, outline, and color while smoothing away the watermark's perturbation. Fine-tuning the already-watermarked model on as few as ten such regenerated images converts true positives into false negatives: DIAGNOSIS's detection accuracy falls from 100% to 50% on all three datasets, and memorization strength drops from near 1 to roughly 0.2-0.5, while FID scores remain comparable or improve.

Load-bearing premise

The method assumes that regenerating an image from 60% noise removes everything the watermark detector keys on while keeping everything the model needs, and that a single empirically chosen gamma value of 0.6 works across settings.

Editorial extensions

If this is right

  • If RATTAN is correct, watermark-based detection methods like DIAGNOSIS can be reduced to chance-level accuracy with only ten regenerated training images.
  • Common image transformations such as Gaussian blur, JPEG compression, and color jittering do not remove DIAGNOSIS's watermark, but controlled regeneration does, so defenses built on pixel-level robustness are not sufficient.
  • Because FID scores stay comparable or improve after RATTAN, an unauthorized model developer can evade detection without visibly sacrificing generation quality.
  • The attack works across Pokemon, Naruto, and CelebA datasets and across Stable Diffusion v1.4, v2.0, and v2.1, suggesting the vulnerability is not dataset- or model-specific.
  • The paper's conclusion is that existing watermark-based protections need redesign to resist adversaries who can regenerate and fine-tune rather than merely transform images.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • As an extension of the paper's logic, any watermark that lives only in high-frequency or pixel-level detail is likely vulnerable to a regeneration-and-fine-tune attack, while watermarks embedded in latent or semantic feature space may resist it.
  • A testable extension would fix the regeneration strength gamma without access to the detector, using only image-quality heuristics, to see whether evasion persists in the paper's stated threat model where the adversary does not know the detector.
  • The same coarse-preserving regeneration idea could plausibly adapt to language models, replacing partial noising with paraphrasing or summarization before fine-tuning, although the paper does not demonstrate this.
  • Adversaries could also vary the regeneration seed or the number of cleaned samples per protected image to trade off evasion rate against fine-grained fidelity, which the paper does not explore.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes RATTAN, an attack against watermark-based protections for text-to-image diffusion models. RATTAN first applies controlled image generation to protected images by partially diffusing them (adding noise up to a fraction gamma of the full forward process) and then denoising with an off-the-shelf Stable Diffusion model; the resulting 'cleaned' images are used to fine-tune a model that was trained on watermarked data. The authors report that this procedure reduces DIAGNOSIS detection accuracy from 100% to 50% (chance) on Pokemon, Naruto, and CelebA using as few as 10 regenerated images, while preserving FID scores, and that DIAGNOSIS is robust against common image transformations. The paper also tests two other watermarking methods (Luo et al., Yu et al.) and several Stable Diffusion versions in the supplementary material.

Significance. If the central claim holds, the paper demonstrates a practical weakness in a state-of-the-art watermark-based provenance defense, which is a timely and important result for IP protection in generative models. The paper is methodologically transparent in reporting FID, memorization strength, and ablations over sample count, epochs, gamma, and model version; it also includes an honest limitations section. The main empirical result for DIAGNOSIS on Stable Diffusion v1.4 is internally consistent across the reported runs. However, the significance is currently limited by the narrow evaluation (one seriously tested detector, three Stable Diffusion versions, no baseline attack), and the full-data ablation in Table 3 contains an internal inconsistency that directly challenges the watermark-removal premise. I therefore view the work as a useful contribution whose central claim is not yet fully established.

major comments (4)
  1. [Section 5.5, Table 3 (# Sample)] The claim that RATTAN-generated images are watermark-free is contradicted by the full-data ablation: detection is 0% for 5-500 cleaned samples but rises to 20% at 783 samples, and memorization strength jumps from 0.253 (10 samples) to 0.640 (783 samples). If the generated images truly contained no watermark features, increasing the number of cleaned fine-tuning images should not reintroduce detectability. The paper's explanation that larger training sets 'might increase the risk of overfitting to the watermarked features' does not resolve this, because the premise is that no watermark features remain. A more likely reading is that partial diffusion at gamma=0.6 retains a weak residual watermark signal, and the 10-image success is due to catastrophic forgetting during fine-tuning rather than successful cleaning. This distinction is load-bearing because the threat model is an unauthorized developer who would typically use the full protected corpus. Please provide a direct measurement of watermark presence in RATTAN-generated images (e.g., running the DIAGNOSIS detector on the cleaned images themselves) and an experiment that separates cleaning efficacy from forgetting, such as fine-tuning on 783 cleaned images and comparing detection against fine-tuning on 783 unmodified watermarked images.
  2. [Threat Model (Section 3) vs. Method (Sections 4.2 and 5.5)] The threat model states that the adversary 'does not have access to the watermark detector developed by the defender,' yet Section 5.5 selects gamma=0.6 because it 'provides the best trade-off between the quality of generated images and the evasion rate,' and the gamma ablation table is evaluated by detection rate against DIAGNOSIS. This is a circularity: the attack's defining parameter is tuned against the same detector used for the headline evaluation. Please either relax the threat model to allow white-box knowledge of DIAGNOSIS, or provide a principled, detector-independent criterion for choosing gamma (e.g., a perceptual similarity or image-quality constraint) and show that the chosen value is not merely the best among those tested on the target detector.
  3. [Sections 5.2 and Table 1] The evaluation of the two watermarking methods besides DIAGNOSIS is not informative as a robustness test: Luo et al. and Yu et al. already achieve only 50% detection accuracy before RATTAN is applied, meaning they are not effective protections in this setting. The paper's broader claim that 'existing state-of-the-art protections are not robust' is therefore supported only by DIAGNOSIS on Stable Diffusion v1.4 (plus v2.0/v2.1 in the supplementary). Please include at least one additional watermarking method that actually achieves high detection on the testbed, and add a baseline such as fine-tuning the watermarked model on the original (uncleaned) images or on randomly perturbed images, so that RATTAN's marginal benefit over naive fine-tuning is quantified.
  4. [Abstract and Section 8] The abstract's claim of evaluating '140 text-to-image diffusion models' overstates the architecture coverage. The experiments report up to 10 trained models per condition, but the number of distinct model architectures tested is three (Stable Diffusion v1.4, v2.0, v2.1, with the main results on v1.4), and the supplementary only adds v2.0/v2.1. Please rephrase the claim to distinguish the number of trained model instances from the number of architectures, or provide a full architecture-level breakdown if the count of 140 refers to something else.
minor comments (6)
  1. [Section 5.1 and Table 1] The experimental setup says 'we train 10 models for both clean and watermarked models,' but Table 1 reports 5 malicious and 5 benign models per method, while Table 2 reports 10 each. Please make the model counts consistent across the tables and text.
  2. [Section 5.4 and Figure 2] The visual comparison in Figure 2 is helpful, but the caption and text do not state whether the image is a representative example or the median output; with only one example it is hard to assess the consistency of watermark removal.
  3. [Section 5.5, Training from scratch] The paragraph on training from scratch reports FID=269.64 and detection=10% but these values are not included in Table 3; please add them to the table or clarify why they are omitted.
  4. [Throughout] There are several minor typos and formatting issues: 'Langauge' in Section 5.1, inconsistent rendering of 'Pokémon' versus 'Pok ´emon', duplicate reference entries for [39] and [40] (both the Pokemon BLIP captions URL), and the phrase 'We leverage experimental exploration to future work' in the Limitations section is ungrammatical.
  5. [Section 5.3 and Table 3] The image-transformation evaluation reports only detection rates and memorization, without FID for the 'Image Transform' rows; adding FID or a similar quality metric would help assess whether the transformations degrade image quality, which is relevant to the threat model.
  6. [Section 4.2] The notation 'γ · t iterations' is clear, but the paper does not specify how the total number of diffusion steps t is chosen or whether it is the same as the 60 steps mentioned in the supplementary; please clarify the relationship between γ, t, and the 60-step default.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: RATTAN's claims are empirical attack results against an external detector, not derivations that reduce to their own inputs.

full rationale

The paper's central claim is that partial diffusion followed by fine-tuning on a small set of regenerated images reduces DIAGNOSIS's detection accuracy from 100% to 50%. This claim is evaluated against an external detector designed by different authors (DIAGNOSIS, Wang et al. [53]), and the method is not defined in terms of that detector's output. The closest thing to fitting is the empirical choice of the noise-level parameter gamma: the paper states, 'Empirically, we choose γ = 0.6 as it provides the best trade-off between the quality of generated images and the evasion rate.' This is honest hyperparameter tuning rather than a disguised prediction; the paper reports a full ablation over gamma values, and the resulting detection rate is presented as an achieved outcome of a tuned configuration, not as a parameter-free derivation. Similarly, the assertion that RATTAN-generated images are 'free of watermarks' is a premise of the method, but it is not established by defining the watermark in terms of the detector's own output; rather, it is an empirical hypothesis tested by the evaluation. The full-data ablation (783 samples, 20% detection, memorization 0.640) does create a substantive internal tension with the 'watermark-free' premise, and the 10-image success may partly reflect forgetting rather than cleaning. However, that is an empirical robustness concern, not circularity: no equation or definition in the paper forces the 10-image result to equal the fitted gamma choice or the detector's metric. The paper also contains no load-bearing self-citations: its cited SDEdit-style controlled generation is attributed to prior independent work, and no uniqueness theorem or author-derived result is invoked to forbid alternatives. Overall, the derivation chain is not circular; the weaknesses are about generalization and internal consistency, which belong to correctness risk rather than circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim relies on several hand-picked hyperparameters (gamma, number of fine-tuning samples, epochs, diffusion steps) and on domain assumptions about separability of watermark features from content. The paper does not ship code, so these parameters cannot be re-audited from artifacts. No new entities are introduced.

free parameters (4)
  • gamma (noise level for controlled generation) = 0.6
    Set empirically as the best trade-off between generated image quality and watermark evasion rate. Ablations in Section 5.5 show detection varies strongly with gamma (80% at 0.2, 0% at 0.6, 50% at 1.0).
  • Number of fine-tuning images = 10
    The paper claims as few as 10 images work. Ablation tests 5, 10, 50, 200, 500, and 783 samples, with 0% detection at 5 to 500 but 20% at 783. The selection procedure for these images is not specified.
  • Number of fine-tuning epochs = 30
    Chosen to balance watermark removal and computational cost. Ablations show epochs 5 to 100 all reach 0% detection, so the exact value is not critical.
  • Diffusion steps for controlled generation = 60
    Default in the generation pipeline per the supplementary material; no justification is given beyond visualizations.
assumptions (4)
  • domain assumption Partial diffusion at gamma=0.6 preserves high-level content while removing low-level watermark features.
    Core mechanism of RATTAN. Supported by visualizations (Figures 2, 4-6) but no quantitative verification that the cleaned images are completely watermark-free.
  • domain assumption Fine-tuning a watermarked model on a small set of cleaned images overwrites watermark memorization without damaging content associations.
    Main empirical mechanism of the attack. The full-data ablation (783 images, 20% detection) suggests residual watermark signal remains in cleaned images, making this assumption fragile.
  • domain assumption The DIAGNOSIS detector and its memorization metric faithfully measure unauthorized data usage.
    The detector and its threshold are inherited from the DIAGNOSIS paper and treated as ground truth without independent validation in this work.
  • domain assumption The off-the-shelf Stable Diffusion model's text conditioning aligns with the protected images' captions well enough for controlled generation.
    The attack relies on text-guided denoising to produce images that match the captions while dropping watermark patterns.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Exploiting Watermark-Based Defense Mechanisms in Text-to-Image Diffusion Models for Unauthorized Data Usage." pith.science (2026). https://pith.science/paper/WYQYARAB

@misc{pith2026241115367,
  author       = {Pith},
  title        = {Pith review of: Exploiting Watermark-Based Defense Mechanisms in Text-to-Image Diffusion Models for Unauthorized Data Usage},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WYQYARAB}},
  note         = {Machine review of arXiv:2411.15367}
}
read the original abstract

Text-to-image diffusion models, such as Stable Diffusion, have shown exceptional potential in generating high-quality images. However, recent studies highlight concerns over the use of unauthorized data in training these models, which may lead to intellectual property infringement or privacy violations. A promising approach to mitigate these issues is to apply a watermark to images and subsequently check if generative models reproduce similar watermark features. In this paper, we examine the robustness of various watermark-based protection methods applied to text-to-image models. We observe that common image transformations are ineffective at removing the watermark effect. Therefore, we propose RATTAN, that leverages the diffusion process to conduct controlled image generation on the protected input, preserving the high-level features of the input while ignoring the low-level details utilized by watermarks. A small number of generated images are then used to fine-tune protected models. Our experiments on three datasets and 140 text-to-image diffusion models reveal that existing state-of-the-art protections are not robust against RATTAN.

Figures

Figures reproduced from arXiv: 2411.15367 by the authors.

Figure 1
Figure 1. The top part represents the existing watermarking procedure for text-to-image diffusion models. The bottom part illustrates [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Comparison of (a) the original image, (b) DIAGNOSIS watermarked image, and the images after applying (c) Color Jittering, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Comparison of images generated by different versions [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Intermediate images during controlled image generation of R [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: Intermediate images during controlled image generation of R [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: Effect of γ on RATTAN’s controlled generation process. 3 [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]
Figure 7
Figure 7. Figure 7: Generated images from the benign model, DIAGNOSIS-watermarked model, and R [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

64 extracted references · 43 canonical work pages

  1. [1]

    stability ai ltd., 23-cv-00201-who

    Andersen v. stability ai ltd., 23-cv-00201-who. https: //www.courtlistener.com/docket/67656604/ andersen-v-stability-ai-ltd/ , 2023. N.D. Cal., Oct. 30, 2023. 1

    work page arXiv 2023

  2. [2]

    https://www.midjourney.com/home,

    Midjourney. https://www.midjourney.com/home,

    work page

  3. [3]

    Image synthesis from an ethical perspective

    Oliver Bendel. Image synthesis from an ethical perspective. AI & SOCIETY, pages 1–10, 2023. 1

    work page 2023

  4. [4]

    Testing human ability to detect ‘deepfake’images of human faces

    Sergi D Bray, Shane D Johnson, and Bennett Kleinberg. Testing human ability to detect ‘deepfake’images of human faces. Journal of Cybersecurity, 9(1):tyad011, 2023. 1

    work page 2023

  5. [5]

    A sur- vey on generative diffusion models

    Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen, Pheng-Ann Heng, and Stan Z Li. A sur- vey on generative diffusion models. IEEE Transactions on Knowledge and Data Engineering, 2024. 2

    work page 2024

  6. [6]

    Extracting training data from diffu- sion models

    Nicolas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagiel- ski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ip- polito, and Eric Wallace. Extracting training data from diffu- sion models. In 32nd USENIX Security Symposium (USENIX Security 23), pages 5253–5270, 2023. 1

    2023

  7. [7]

    Naruto blip captions

    Eole Cervenka. Naruto blip captions. https : / / huggingface . co / datasets / lambdalabs / naruto-blip-captions/, 2022. 5, 1

    work page 2022

  8. [8]

    Understanding and creating art with ai: Review and outlook

    Eva Cetinic and James She. Understanding and creating art with ai: Review and outlook. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 18(2):1–22, 2022. 1

    work page 2022

Show all 64 references
  1. [9]

    Adaptively-realistic image gen- eration from stroke and sketch with diffusion model

    Shin-I Cheng, Yu-Jie Chen, Wei-Chen Chiu, Hung-Yu Tseng, and Hsin-Ying Lee. Adaptively-realistic image gen- eration from stroke and sketch with diffusion model. In Pro- ceedings of the IEEE/CVF Winter Conference on Applica- tions of Computer Vision (WACV), pages 4054–4062, 2023. 1

    2023

  2. [10]

    Diffusion models in vision: A survey

    Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. Diffusion models in vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 45(9):10850–10869, 2023. 2

    2023

  3. [11]

    Diffusionshield: A wa- termark for copyright protection against generative diffusion models

    Yingqian Cui, Jie Ren, Han Xu, Pengfei He, Hui Liu, Lichao Sun, Yue Xing, and Jiliang Tang. Diffusionshield: A wa- termark for copyright protection against generative diffusion models. arXiv preprint arXiv:2306.04642, 2023. 1

    2023 arXiv

  4. [12]

    Diffusion models beat gans on image synthesis

    Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in Neural Informa- tion Processing Systems, 34:8780–8794, 2021. 2

    2021

  5. [13]

    Are diffusion models vulnerable to membership inference attacks? In International Conference on Machine Learning, pages 8717–8730

    Jinhao Duan, Fei Kong, Shiqi Wang, Xiaoshuang Shi, and Kaidi Xu. Are diffusion models vulnerable to membership inference attacks? In International Conference on Machine Learning, pages 8717–8730. PMLR, 2023. 1, 3

    2023

  6. [14]

    Towards more realistic membership inference attacks on large diffusion models

    Jan Dubi ´nski, Antoni Kowalczuk, Stanisław Pawlak, Prze- myslaw Rokita, Tomasz Trzci ´nski, and Paweł Morawiecki. Towards more realistic membership inference attacks on large diffusion models. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision...

    2024

  7. [15]

    Generative text-to-image diffusion for automated map pro- duction based on geosocial media data

    Alexander Dunkel, Dirk Burghardt, and Madalina Gugulica. Generative text-to-image diffusion for automated map pro- duction based on geosocial media data. KN-Journal of Car- tography and Geographic Information, 74(1):3–15, 2024. 1

    2024

  8. [16]

    Re- inforcement learning for fine-tuning text-to-image diffusion models

    Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Moham- mad Ghavamzadeh, Kangwook Lee, and Kimin Lee. Re- inforcement learning for fine-tuning text-to-image diffusion models. Advances in Neural Information Processing Sys- tems, 36, 2024. 3

    2024

  9. [17]

    Ccedit: Creative and controllable video editing via diffu- sion models

    Ruoyu Feng, Wenming Weng, Yanhui Wang, Yuhui Yuan, Jianmin Bao, Chong Luo, Zhibo Chen, and Baining Guo. Ccedit: Creative and controllable video editing via diffu- sion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 6712– 67...

    2024

  10. [18]

    Copyright infringement in ai-generated artworks

    Jessica L Gillotte. Copyright infringement in ai-generated artworks. UC Davis L. Rev., 53:2655, 2019. 1

    2019

  11. [19]

    Logan: Membership inference attacks against generative models

    Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro. Logan: Membership inference attacks against generative models. arXiv preprint arXiv:1705.07663, 2017. 3

    2017 arXiv

  12. [20]

    Gans trained by a two time-scale update rule converge to a local nash equilib- rium, 2018

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilib- rium, 2018. 6

    2018

  13. [21]

    Lora: Low-rank adaptation of large language models

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. 5

    2021 arXiv

  14. [22]

    Evading watermark based detection of ai-generated content

    Zhengyuan Jiang, Jinghuai Zhang, and Neil Zhenqiang Gong. Evading watermark based detection of ai-generated content. In Proceedings of the 2023 ACM SIGSAC Con- ference on Computer and Communications Security , pages 1168–1181, 2023. 1

    2023

  15. [23]

    Variational diffusion models

    Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho. Variational diffusion models. Advances in neural infor- mation processing systems, 34:21696–21707, 2021. 2

    2021

  16. [24]

    A comprehensive review of the latest ad- vancements in large generative ai models

    Satyam Kumar, Dayima Musharaf, Seerat Musharaf, and Anil Kumar Sagar. A comprehensive review of the latest ad- vancements in large generative ai models. InAdvanced Com- munication and Intelligent Systems , pages 90–103, Cham,

  17. [25]

    Unveiling structural memo- rization: Structural membership inference attack for text-to- image diffusion models

    Qiao Li, Xiaomeng Fu, Xi Wang, Jin Liu, Xingyu Gao, Jiao Dai, and Jizhong Han. Unveiling structural memo- rization: Structural membership inference attack for text-to- image diffusion models. InProceedings of the 32nd ACM In- ternational Conference on Multimedia, pages 10554–10562,

  18. [26]

    Diffusion 9 models for image restoration and enhancement – a compre- hensive survey, 2023

    Xin Li, Yulin Ren, Xin Jin, Cuiling Lan, Xingrui Wang, Wenjun Zeng, Xinchao Wang, and Zhibo Chen. Diffusion 9 models for image restoration and enhancement – a compre- hensive survey, 2023. 1

    2023

  19. [27]

    Untargeted backdoor watermark: Towards harm- less and stealthy dataset copyright protection

    Yiming Li, Yang Bai, Yong Jiang, Yong Yang, Shu-Tao Xia, and Bo Li. Untargeted backdoor watermark: Towards harm- less and stealthy dataset copyright protection. Advances in Neural Information Processing Systems , 35:13238–13250,

  20. [28]

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

    Yixin Liu, Chenrui Fan, Yutong Dai, Xun Chen, Pan Zhou, and Lichao Sun. Metacloak: Preventing unauthorized subject-driven text-to-image diffusion-based synthesis via meta-learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2421...

    2024

  21. [29]

    Deep learning face attributes in the wild

    Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision, pages 3730–3738, 2015. 5, 1

    2015

  22. [30]

    Yiwei Lu, Matthew Y . R. Yang, Zuoqiu Liu, Gautam Ka- math, and Yaoliang Yu. Disguised copyright infringement of latent diffusion models, 2024. 1

    2024

  23. [31]

    Leveraging optimization for adaptive attacks on image watermarks

    Nils Lukas, Abdulrahman Diaa, Lucas Fenaux, and Florian Kerschbaum. Leveraging optimization for adaptive attacks on image watermarks. In The Twelfth International Confer- ence on Learning Representations, 2024. 1

    2024

  24. [32]

    Steal my artworks for fine- tuning? a watermarking framework for detecting art theft mimicry in text-to-image models, 2023

    Ge Luo, Junqiang Huang, Manman Zhang, Zhenxing Qian, Sheng Li, and Xinpeng Zhang. Steal my artworks for fine- tuning? a watermarking framework for detecting art theft mimicry in text-to-image models, 2023. 1, 3, 5, 6

    2023

  25. [33]

    Membership inference attacks against diffusion models

    Tomoya Matsumoto, Takayuki Miura, and Naoto Yanai. Membership inference attacks against diffusion models. In 2023 IEEE Security and Privacy Workshops (SPW) , pages 77–83. IEEE, 2023. 1

    2023

  26. [34]

    Sdedit: Guided image synthesis and editing with stochastic differential equa- tions

    Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jia- jun Wu, Jun-Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equa- tions. In International Conference on Learning Representa- tions, 2022. 5

    2022

  27. [35]

    Fine-tuning diffusion models with limited data

    Taehong Moon, Moonseok Choi, Gayoung Lee, Jung-Woo Ha, and Juho Lee. Fine-tuning diffusion models with limited data. In NeurIPS 2022 Workshop on Score-Based Methods,

    2022

  28. [36]

    Glide: Towards photorealistic image generation and editing with text-guided diffusion models.arXiv preprint arXiv:2112.10741, 2021

    Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models.arXiv preprint arXiv:2112.10741, 2021. 1

    2021 arXiv

  29. [37]

    Black-box membership in- ference attacks against fine-tuned diffusion models

    Yan Pang and Tianhao Wang. Black-box membership in- ference attacks against fine-tuned diffusion models. arXiv preprint arXiv:2312.08207, 2023. 1

    2023 arXiv

  30. [38]

    On the use of stable diffusion for creating realistic faces: from generation to detection

    Lorenzo Papa, Lorenzo Faiella, Luca Corvitto, Luca Maiano, and Irene Amerini. On the use of stable diffusion for creating realistic faces: from generation to detection. In2023 11th In- ternational Workshop on Biometrics and Forensics (IWBF), pages 1–6, 2023. 2

    2023

  31. [39]

    Justin N. M. Pinkney. Pokemon blip captions. https: / / huggingface . co / datasets / lambdalabs / pokemon-blip-captions/, 2022. 1

    2022

  32. [40]

    Justin N. M. Pinkney. Pokemon blip captions. https: / / huggingface . co / datasets / lambdalabs / pokemon-blip-captions/, 2022. 5

    2022

  33. [41]

    Aimon Rahman, Jeya Maria Jose Valanarasu, Ilker Haci- haliloglu, and Vishal M. Patel. Ambiguous medical im- age segmentation using diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11536–11546, 2023. 1

    2023

  34. [42]

    Zero-shot text-to-image generation, 2021

    Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea V oss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation, 2021. 1

    2021

  35. [43]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10684–10695, 2022. 1, 2, 4, 5

    2022

  36. [44]

    Robustness of ai-image detectors: Fundamental limits and practical attacks

    Mehrdad Saberi, Vinu Sankar Sadasivan, Keivan Rezaei, Aounon Kumar, Atoosa Chegini, Wenxiao Wang, and Soheil Feizi. Robustness of ai-image detectors: Fundamental limits and practical attacks. In The Twelfth International Confer- ence on Learning Representations, 2024. 1

    2024

  37. [45]

    Photorealistic text-to-image diffusion models with deep language understanding

    Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information...

    2022

  38. [46]

    Laion-5b: An open large-scale dataset for training next generation image-text models

    Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Worts- man, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural In- f...

    2022

  39. [47]

    Glaze: Protecting artists from style mimicry by {Text-to-Image} models

    Shawn Shan, Jenna Cryan, Emily Wenger, Haitao Zheng, Rana Hanocka, and Ben Y Zhao. Glaze: Protecting artists from style mimicry by {Text-to-Image} models. In 32nd USENIX Security Symposium (USENIX Security 23) , pages 2187–2204, 2023. 3

    2023

  40. [48]

    Finetuning text-to- image diffusion models for fairness

    Xudong Shen, Chao Du, Tianyu Pang, Min Lin, Yongkang Wong, and Mohan Kankanhalli. Finetuning text-to- image diffusion models for fairness. arXiv preprint arXiv:2311.07604, 2023. 3

    2023 arXiv

  41. [49]

    Membership inference attacks against machine learning models

    Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP), pages 3–18. IEEE, 2017. 1

    2017

  42. [50]

    Understanding and mitigating copying in diffusion models

    Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Understanding and mitigating copying in diffusion models. Advances in Neural Informa- tion Processing Systems, 36:47783–47803, 2023. 1

    2023

  43. [51]

    Synthetic image verification in the era of generative ai: What works and what isn’t there yet, 2024

    Diangarti Tariang, Riccardo Corvi, Davide Cozzolino, Gio- vanni Poggi, Koki Nagano, and Luisa Verdoliva. Synthetic image verification in the era of generative ai: What works and what isn’t there yet, 2024. 1

    2024

  44. [52]

    Anti-dreambooth: Pro- tecting users from personalized text-to-image synthesis

    Thanh Van Le, Hao Phung, Thuan Hoang Nguyen, Quan Dao, Ngoc N Tran, and Anh Tran. Anti-dreambooth: Pro- tecting users from personalized text-to-image synthesis. In 10 Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2116–2127, 2023. 3

    2023

  45. [53]

    Diagnosis: Detecting unautho- rized data usages in text-to-image diffusion models

    Zhenting Wang, Chen Chen, Lingjuan Lyu, Dimitris N Metaxas, and Shiqing Ma. Diagnosis: Detecting unautho- rized data usages in text-to-image diffusion models. In The Twelfth International Conference on Learning Representa- tions, 2023. 1, 3, 5, 6

    2023

  46. [54]

    Creative painting with latent diffusion mod- els

    Xianchao Wu. Creative painting with latent diffusion mod- els. In Proceedings of the Second Workshop on When Cre- ative AI Meets Conversational AI , pages 59–80, Gyeongju, Republic of Korea, 2022. Association for Computational Linguistics. 1

    2022

  47. [55]

    Diffusion models: A comprehensive survey of methods and applications

    Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Run- sheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming- Hsuan Yang. Diffusion models: A comprehensive survey of methods and applications. ACM Computing Surveys, 56(4): 1–39, 2023. 2

    2023

  48. [56]

    Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization, 2024

    Tao Yang, Rongyuan Wu, Peiran Ren, Xuansong Xie, and Lei Zhang. Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization, 2024. 1

    2024

  49. [57]

    Artificial fingerprinting for generative models: Root- ing deepfake attribution in training data

    Ning Yu, Vladislav Skripniuk, Sahar Abdelnabi, and Mario Fritz. Artificial fingerprinting for generative models: Root- ing deepfake attribution in training data. In Proceedings of the IEEE/CVF International conference on computer vision, pages 14448–14457, 2021. 1, 3, 5, 6

    2021

  50. [58]

    Adding conditional control to text-to-image diffusion models

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836–3847, 2023. 2

    2023

  51. [59]

    On copyright risks of text-to-image diffusion models

    Yang Zhang, Teoh Tze Tzun, Lim Wei Hern, and Kenji Kawaguchi. On copyright risks of text-to-image diffusion models. In ECCV 2024 Workshop The Dark Side of Genera- tive AIs and Beyond, 2024. 1

    2024

  52. [60]

    Invisible image watermarks are provably removable using generative ai

    Xuandong Zhao, Kexun Zhang, Zihao Su, Saastha Vasan, Ilya Grishchenko, Christopher Kruegel, Giovanni Vigna, Yu- Xiang Wang, and Lei Li. Invisible image watermarks are provably removable using generative ai. Advances in Neural Information Processing Systems, 2024. 1 11 Exploiti...

    2024

  53. [62]

    • Pok ´emon [39]: This dataset consists of 833 text-image pairs

    Dataset Details In this section, we provide more information about the datasets utilized in this work. • Pok ´emon [39]: This dataset consists of 833 text-image pairs. The captions for the images were generated using the BLIP model. • Naruto [7]: This dataset contains 1,121 te...

  54. [63]

    In this section, we evaluate the efficacy of RATTAN against other popular models, including Stable Diffusion v2.0 and Stable Diffusion v2.1

    Evaluation on Different Models The experiments in Section 5 of the main text are conducted on Stable Diffusion v1.4. In this section, we evaluate the efficacy of RATTAN against other popular models, including Stable Diffusion v2.0 and Stable Diffusion v2.1. The results, report...

  55. [64]

    Visualizations In this section, we present visualizations of images gener- ated during the controlled generation process of R ATTAN, along with visualizations of images produced by the trained text-to-image models. 9.1. Controlled Generation Diffusion Process RATTAN utilizes t...

  56. [2023]

    Springer Nature Switzerland. 1

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.