# Dayal, Garg and Sen introduce image attribution across Stable Diffusion fine-tunes [Canonical reading](https://escapemeridian.com/readings/dayal-garg-and-sen-introduce-image-attribution-across-stable-0bed55a0) Ashutosh Dayal, Mallika Garg and Debashis Sen introduce IntrAA-SD, a task for attributing an image to a fine-tuned model when candidate models share the same architectural backbone. They describe it as addressing a gap in image source attribution: existing datasets and systems mostly target architectural signatures, while fine-tunes such as Juggernaut XL and RealVis XL differ through fine-tuning of SDXL. The authors provide a benchmark dataset of 140K generated images, with in-domain and out-of-distribution prompts, and a baseline method. That method uses a style-conditioned Mixture-of-Experts network, a frozen CLIP backbone, and a high-resolution LoRA-adapted DINOv2 network. The paper says it evaluates attribution accuracy on its dataset and reports ablation studies. The work treats attribution among models with a shared architecture as a distinct research task, rather than assuming an image’s source can be identified from architectural differences. Its practical significance depends on how well the baseline performs beyond the dataset and settings described; the abstract gives no accuracy results. The authors say code and the dataset are available. Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv cs.AI · Article Original source date: Nov 4, 2026 (2026-11-04). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-10T02:48:16.560Z. First observed: 2026-11-05T05:06:39.904Z. Retrieved: 2026-11-10T02:47:19.758Z. - [Intra-Architecture Source Attribution for Stable Diffusion: A New Task with Benchmark Dataset and Baseline](https://arxiv.org/abs/2611.06000) ## Limits The abstract says the method is evaluated for attribution accuracy but gives no accuracy results, so its performance cannot be assessed from this evidence. The evidence is the paper's abstract; it does not include the paper's detailed methods or results. ## Open question What attribution accuracy does the baseline achieve on in-domain and out-of-distribution prompts? ## Source-stated The authors introduce IntrAA-SD to attribute generated images among Stable Diffusion models that share an architectural backbone and differ through fine-tuning. > the source models to be differentiated share the same architectural backbone and differ only due to fine-tuning. - [Supporting source](https://arxiv.org/abs/2611.06000) ## Source-stated The authors provide a benchmark dataset of 140K images generated from different SDXL fine-tunes, with in-domain and out-of-distribution prompts. > We provide a first-of-its-kind $140$K-image benchmark dataset containing images generated from different fine-tunes of SDXL, along with in-domain and out-of-distribution prompts. - [Supporting source](https://arxiv.org/abs/2611.06000) ## Source-stated The baseline uses a style-conditioned Mixture-of-Experts network, a frozen CLIP backbone, and a high-resolution LoRA-adapted DINOv2 network. > It uses a frozen CLIP backbone to capture the image style and gate the MoE, which processes fine-tuned trace features from a high-resolution LoRA-adapted DINOv2 network. - [Supporting source](https://arxiv.org/abs/2611.06000) ## Source-stated The authors say they evaluate attribution accuracy on their dataset and perform ablation studies. > Our baseline intra-architecture source attribution method is evaluated in terms of attribution accuracy on our dataset, and ablation studies are also performed. - [Supporting source](https://arxiv.org/abs/2611.06000) ## Source-stated The authors say the code and dataset are available. > Code and dataset is available at this https URL. - [Supporting source](https://arxiv.org/abs/2611.06000) ## Source and contributor context Dayal, Ashutosh, Garg, Mallika, Sen, Debashis · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Fine-tuning Further training of an existing model to adapt it to a particular task or use. - [Definition reference](https://developers.google.com/machine-learning/glossary#fine-tuning) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## Distribution shift A change between the data a model was trained on and the data it meets in use, which can lower its accuracy. - [Definition reference](https://en.wikipedia.org/wiki/Domain_adaptation) ## Mixture of experts A model design that sends each input to a few specialized sub-networks instead of the whole model, which saves computation. - [Definition reference](https://developers.google.com/machine-learning/glossary#mixture-of-experts) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 813776ea-09a3-4747-85e1-2fd3e65fa30f. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-10T02:48:16.560Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: bbeabec2d19c6b4440004cd7ea7fc82b59fcbcfa0961a3c617f57db66bfcaa59. Source text hash: 730f87c43ec054c5c6a9147a5f7c12619343cda5a873dd84fbc3bfbac18f7576. ## Revision and source-check history ### Version 1 — 2026-11-10T02:48:16.560Z Ashutosh Dayal, Mallika Garg and Debashis Sen introduce IntrAA-SD, a task for attributing an image to a fine-tuned model when candidate models share the same architectural backbone. They describe it as addressing a gap in image source attribution: existing datasets and systems mostly target architectural signatures, while fine-tunes such as Juggernaut XL and RealVis XL differ through fine-tuning of SDXL. The authors provide a benchmark dataset of 140K generated images, with in-domain and out-of-distribution prompts, and a baseline method. That method uses a style-conditioned Mixture-of-Experts network, a frozen CLIP backbone, and a high-resolution LoRA-adapted DINOv2 network. The paper says it evaluates attribution accuracy on its dataset and reports ablation studies. The work treats attribution among models with a shared architecture as a distinct research task, rather than assuming an image’s source can be identified from architectural differences. Its practical significance depends on how well the baseline performs beyond the dataset and settings described; the abstract gives no accuracy results. The authors say code and the dataset are available. ## Original source arXiv cs.AI · Article Original source date: Nov 4, 2026 (2026-11-04). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-10T02:48:16.560Z. First observed: 2026-11-05T05:06:39.904Z. Retrieved: 2026-11-10T02:47:19.758Z. - [Intra-Architecture Source Attribution for Stable Diffusion: A New Task with Benchmark Dataset and Baseline](https://arxiv.org/abs/2611.06000) ## Limits The abstract says the method is evaluated for attribution accuracy but gives no accuracy results, so its performance cannot be assessed from this evidence. The evidence is the paper's abstract; it does not include the paper's detailed methods or results. ## Open question What attribution accuracy does the baseline achieve on in-domain and out-of-distribution prompts? ## Source-stated The authors introduce IntrAA-SD to attribute generated images among Stable Diffusion models that share an architectural backbone and differ through fine-tuning. > the source models to be differentiated share the same architectural backbone and differ only due to fine-tuning. - [Supporting source](https://arxiv.org/abs/2611.06000) ## Source-stated The authors provide a benchmark dataset of 140K images generated from different SDXL fine-tunes, with in-domain and out-of-distribution prompts. > We provide a first-of-its-kind $140$K-image benchmark dataset containing images generated from different fine-tunes of SDXL, along with in-domain and out-of-distribution prompts. - [Supporting source](https://arxiv.org/abs/2611.06000) ## Source-stated The baseline uses a style-conditioned Mixture-of-Experts network, a frozen CLIP backbone, and a high-resolution LoRA-adapted DINOv2 network. > It uses a frozen CLIP backbone to capture the image style and gate the MoE, which processes fine-tuned trace features from a high-resolution LoRA-adapted DINOv2 network. - [Supporting source](https://arxiv.org/abs/2611.06000) ## Source-stated The authors say they evaluate attribution accuracy on their dataset and perform ablation studies. > Our baseline intra-architecture source attribution method is evaluated in terms of attribution accuracy on our dataset, and ablation studies are also performed. - [Supporting source](https://arxiv.org/abs/2611.06000) ## Source-stated The authors say the code and dataset are available. > Code and dataset is available at this https URL. - [Supporting source](https://arxiv.org/abs/2611.06000) ## Source and contributor context Dayal, Ashutosh, Garg, Mallika, Sen, Debashis · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Fine-tuning Further training of an existing model to adapt it to a particular task or use. - [Definition reference](https://developers.google.com/machine-learning/glossary#fine-tuning) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## Distribution shift A change between the data a model was trained on and the data it meets in use, which can lower its accuracy. - [Definition reference](https://en.wikipedia.org/wiki/Domain_adaptation) ## Mixture of experts A model design that sends each input to a few specialized sub-networks instead of the whole model, which saves computation. - [Definition reference](https://developers.google.com/machine-learning/glossary#mixture-of-experts) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 813776ea-09a3-4747-85e1-2fd3e65fa30f. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-10T02:48:16.560Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: bbeabec2d19c6b4440004cd7ea7fc82b59fcbcfa0961a3c617f57db66bfcaa59. Source text hash: 730f87c43ec054c5c6a9147a5f7c12619343cda5a873dd84fbc3bfbac18f7576. ### passed — 2026-11-10T02:48:16.560Z — anthropic/claude-opus-5.5 Publication: 813776ea-09a3-4747-85e1-2fd3e65fa30f attribution — pass: The reading correctly credits Ashutosh Dayal, Mallika Garg and Debashis Sen. The task, dataset and baseline are framed as the authors' claims rather than as established facts. > Authors:Ashutosh Dayal, Mallika Garg, Debashis Sen summary — pass: The title, summary and question faithfully state what the paper introduces: IntrAA-SD, a 140K-image SDXL fine-tune dataset, and a baseline. It explains the gap the authors claim, which is that prior work targets architectural signatures. It also notes that the abstract reports no accuracy figures. Technical terms are kept exactly and can be explained through tooltips. The question follows directly from the missing results. > Most existing datasets and systems related to image source attribution are targeted towards the detection of architectural signatures of the source models. date — pass: The originalPublished date of 2026-11-04 matches the arXiv v1 submission date. It is not the later feed date of 2026-11-05. > \[Submitted on 4 Nov 2026\] support — pass: Each claim is directly supported by the abstract. The interpretive sentence about treating shared-architecture attribution as a distinct task restates the authors' framing. The stated limitations, including that no accuracy results are given, are accurate and do not overstate performance. The reading invents no benefits, costs or forecasts. > Our baseline intra-architecture source attribution method is evaluated in terms of attribution accuracy on our dataset, and ablation studies are also performed. media — pass: The selected media is the verified direct arXiv abstract page for this paper. That is the most direct way to reach the source. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/0bed55a064a44571d28ef393fed6fa0add698840d1b4a1348814bce42a689f6d/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/dayal-garg-and-sen-introduce-image-attribution-across-stable-0bed55a0.json)