Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach | Paper Citation Record

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This paper cites Ntire 2017 challenge on single image super-resolution: Dataset and study.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Ntire 2017 challenge on single image super-resolution: Dataset and study

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This paper cites Variational image compres- sion with a scale hyperprior.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Variational image compres- sion with a scale hyperprior

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This paper cites Cold diffusion: Inverting arbitrary im- age transforms without noise.Advances in Neural Informa- tion Processing Systems, 36:41259–41282, 2023.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Cold diffusion: Inverting arbitrary im- age transforms without noise.Advances in Neural Informa- tion Processing Systems, 36:41259–41282, 2023

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This paper cites Trustmark: Robust watermarking and watermark removal for arbitrary resolution images.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Trustmark: Robust watermarking and watermark removal for arbitrary resolution images

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This paper cites Learned image compression with discretized gaussian mixture likelihoods and attention modules.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Learned image compression with discretized gaussian mixture likelihoods and attention modules

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This paper cites Digital watermarking.Journal of Electronic Imaging, 11(3):414–414, 2002.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Digital watermarking.Journal of Electronic Imaging, 11(3):414–414, 2002

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This paper cites Irnext: rethinking convolutional network design for image restoration.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Irnext: rethinking convolutional network design for image restoration

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This paper cites Revitalizing convolutional network for image restoration.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Revitalizing convolutional network for image restoration

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This paper cites Ft-shield: A watermark against unauthorized fine-tuning in text-to-image diffusion models.ACM SIGKDD Explorations Newsletter, 26(2):76–88, 2025.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Ft-shield: A watermark against unauthorized fine-tuning in text-to-image diffusion models.ACM SIGKDD Explorations Newsletter, 26(2):76–88, 2025

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This paper cites Diffusionshield: A water- mark for data copyright protection against generative diffu- sion models.ACM SIGKDD Explorations Newsletter, 26(2): 60–75, 2025.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Diffusionshield: A water- mark for data copyright protection against generative diffu- sion models.ACM SIGKDD Explorations Newsletter, 26(2): 60–75, 2025

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This paper cites Freecustom: Tuning- free customized image generation for multi-concept compo- sition.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Freecustom: Tuning- free customized image generation for multi-concept compo- sition

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This paper cites Are diffusion models vulnerable to membership inference attacks? InInternational Conference on Machine Learning, pages 8717–8730.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Are diffusion models vulnerable to membership inference attacks? InInternational Conference on Machine Learning, pages 8717–8730

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This paper cites Cdi: Copyrighted data identification in dif- fusion models.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Cdi: Copyrighted data identification in dif- fusion models

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This paper cites The stable signature: Rooting watermarks in latent diffusion models.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach The stable signature: Rooting watermarks in latent diffusion models

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

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

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This paper cites Svdiff: Compact param- eter space for diffusion fine-tuning.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Svdiff: Compact param- eter space for diffusion fine-tuning

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Observation 84a38d06-2334-419c-b79e-bac53c1d9297 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017

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This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Unresolved cited work

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Multi-concept customization of text-to-image diffusion

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This paper cites Towards reli- able verification of unauthorized data usage in personalized text-to-image diffusion models.

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Towards reli- able verification of unauthorized data usage in personalized text-to-image diffusion models

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Swinir: Image restoration us- ing swin transformer

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Deep learning face attributes in the wild

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Waterloo ex- 9 ploration database: New challenges for image quality assess- ment models.IEEE Transactions on Image Processing, 26 (2):1004–1016, 2016

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Generative Watermarking Against Unauthorized Subject-Driven Image Synthesis

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Dwt-dct-svd based watermark- ing

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Unresolved cited work

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Entruth: Enhanc- ing the traceability of unauthorized dataset usage in text-to- image diffusion models with minimal and robust alterations

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach High-resolution image synthesis with latent diffusion models

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Combinational image wa- termarking in the spatial and frequency domains.Pattern Recognition, 36(4):969–975, 2003

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Deep unsupervised learning using nonequilibrium thermodynamics

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Improved techniques for training score-based generative models.Advances in neural information processing systems, 33:12438–12448, 2020

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Denet: Disen- tangled embedding network for visible watermark removal

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Stegastamp: Invisible hyperlinks in physical photographs

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Ex- ploring clip for assessing the look and feel of images

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Diagnosis: Detecting unau- thorized data usages in text-to-image diffusion models

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Powerful and flexible: Personalized text- to-image generation via reinforcement learning

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Tree-rings watermarks: Invisible fingerprints for diffusion images.Advances in Neural Information Process- ing Systems, 36:58047–58063, 2023

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach High- capacity convolutional video steganography with temporal residual modeling

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Wikiart: Visual art encyclopedia.https://www

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Artificial fingerprinting for generative models: Root- ing deepfake attribution in training data

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Invisible steganog- raphy via generative adversarial networks.Multimedia tools and applications, 78(7):8559–8575, 2019

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Invisible image watermarks are provably removable using generative ai.Advances in neural information processing systems, 37:8643–8672, 2024

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach A Recipe for Watermarking Diffusion Models

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Hidden: Hiding data with deep networks

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Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach Watermark-embedded adversarial examples for copyright protection against diffusion models

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