Dayal, Ashutosh, Garg, Mallika, Sen, Debashis · Nov 4, 2026
Dayal, Garg and Sen introduce image attribution across Stable Diffusion fine-tunes
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Ashutosh Dayal, Mallika Garg and Debashis Sen introduce IntrAA-SD, a task for attributing an image to a 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 dataset of 140K generated images, with in-domain and prompts, and a baseline method. That method uses a style-conditioned 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
cs.AI · Article
Source details
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.
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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 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.
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.
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.
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.
Source-stated
The authors say the code and dataset are available.
Code and dataset is available at this https URL.
Source and contributor context
Dayal, Ashutosh, Garg, Mallika, Sen, Debashis · Article author
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Source media
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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.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Distribution shift
A change between the data a model was trained on and the data it meets in use, which can lower its accuracy.
Mixture of experts
A model design that sends each input to a few specialized sub-networks instead of the whole model, which saves computation.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
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
Dayal, Garg and Sen introduce image attribution across Stable Diffusion fine-tunes
Ashutosh Dayal, Mallika Garg and Debashis Sen introduce IntrAA-SD, a task for attributing an image to a 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 dataset of 140K generated images, with in-domain and prompts, and a baseline method. That method uses a style-conditioned 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
cs.AI · Article
Source details
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.
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 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.
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.
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.
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.
Source-stated
The authors say the code and dataset are available.
Code and dataset is available at this https URL.
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.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Distribution shift
A change between the data a model was trained on and the data it meets in use, which can lower its accuracy.
Mixture of experts
A model design that sends each input to a few specialized sub-networks instead of the whole model, which saves computation.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
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.
Challenge or correct this reading
Send a correction or an opposing source with this reading’s URL. Disagreement and uncertainty can remain unresolved.
Further training of an existing model to adapt it to a particular task or use.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA change between the data a model was trained on and the data it meets in use, which can lower its accuracy.
Definition referenceA model design that sends each input to a few specialized sub-networks instead of the whole model, which saves computation.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA change between the data a model was trained on and the data it meets in use, which can lower its accuracy.
Definition referenceFurther training of an existing model to adapt it to a particular task or use.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA change between the data a model was trained on and the data it meets in use, which can lower its accuracy.
Definition referenceA model design that sends each input to a few specialized sub-networks instead of the whole model, which saves computation.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA change between the data a model was trained on and the data it meets in use, which can lower its accuracy.
Definition reference