Paper Citation Record · LEDGER

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples

As of 7 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 2 inbound Pith citation observations for arXiv:2509.25682.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.25682 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:44:11.471857Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T01:04:35.510933Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-14T19:39:23.872435Z

Reference resolution

54 of 54 outbound references displayed

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External citation measurements

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Outbound references

Observation 7061cd69-7bbc-4ba7-aff1-af85139020bd · outbound

This paper cites Con- trasting deepfakes diffusion via contrastive learning and global-local similarities.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Con- trasting deepfakes diffusion via contrastive learning and global-local similarities

Reference 4

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Observation b008a3b2-2d86-4719-bcda-f951c4215679 · outbound

This paper cites Raising the bar of ai-generated image detection with CLIP.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Raising the bar of ai-generated image detection with CLIP

Reference 5

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Observation 29be766b-de08-4b29-89f1-28adc3fe4783 · outbound

This paper cites Emerging Properties in Unified Multimodal Pretraining.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Emerging Properties in Unified Multimodal Pretraining

Reference 6

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Observation 4ba9eb43-61fe-496f-9ac7-8e328c530e52 · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 7

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Observation e3f05b50-55fd-4fb4-8d43-fd7f80b6b020 · outbound

This paper cites Toward Generalizable Forgery Detection and Reasoning.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Toward Generalizable Forgery Detection and Reasoning

Reference 9

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Observation 10366f7e-3515-4185-b764-926262851966 · outbound

This paper cites Wukong: A 100 million large- scale chinese cross-modal pre-training benchmark.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Wukong: A 100 million large- scale chinese cross-modal pre-training benchmark

Reference 11

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Observation fc28b73c-c9e3-4409-bb99-b472223c86c7 · outbound

This paper cites Denoising diffusion probabilistic models.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Denoising diffusion probabilistic models

Reference 12

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Observation 9309ac86-1935-425b-97a2-f5caaa5c451a · outbound

This paper cites Progressive growing of gans for im- proved quality, stability, and variation.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Progressive growing of gans for im- proved quality, stability, and variation

Reference 13

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Observation 37ccd311-6343-46c1-921a-b5e96caee0ab · outbound

This paper cites Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding

Reference 16

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Observation aca39f43-31b8-4937-8d56-ccf68ee59dcf · outbound

This paper cites UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation

Reference 17

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Observation c0b2244c-92e8-4036-8fc5-55c34812964f · outbound

This paper cites Which model generated this image? A model-agnostic approach for origin attribution.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Which model generated this image? A model-agnostic approach for origin attribution

Reference 18

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Observation 42dd3655-a5d4-41f8-9f6a-9028ac00b685 · outbound

This paper cites A convnet for the 2020s.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples A convnet for the 2020s

Reference 19

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Observation 4da0a3bc-24af-4c74-996b-9f837da9af5b · outbound

This paper cites Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images

Reference 20

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Observation f7b316a2-429e-4470-a980-852654ff2ec6 · outbound

This paper cites Community forensics: Using thousands of generators to train fake image detectors.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Community forensics: Using thousands of generators to train fake image detectors

Reference 22

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Observation b79cefbc-b258-428f-941c-f32ec2bb6804 · outbound

This paper cites ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection

Reference 23

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Observation e02ce1d1-0bb2-41eb-825b-fdca1eb14996 · outbound

This paper cites Stay-Positive: A Case for Ignoring Real Image Features in Fake Image Detection.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Stay-Positive: A Case for Ignoring Real Image Features in Fake Image Detection

Reference 24

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Observation 2ab14176-4aef-4db6-afbd-e9c03295de24 · outbound

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

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 25

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Observation 858c023c-ef21-4e52-ad21-eacbf87b3dc5 · outbound

This paper cites an unresolved cited work.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Unresolved cited work

Reference 26

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Observation 9aa8fcaa-e8e8-4d97-a113-20ce07c51405 · outbound

This paper cites Contrastive pseudo learning for open-world deepfake attribution.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Contrastive pseudo learning for open-world deepfake attribution

Reference 27

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Observation 0d5c70af-e906-49f6-8155-5736dd3b0706 · outbound

This paper cites Rethinking the up-sampling operations in cnn-based generative network for generalizable deep- fake detection.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Rethinking the up-sampling operations in cnn-based generative network for generalizable deep- fake detection

Reference 28

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Observation e4e1981a-d8f5-4e2e-a892-9b6253aac7b7 · outbound

This paper cites Ovis-U1 Technical Report.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Ovis-U1 Technical Report

Reference 29

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Observation e36c8167-3170-4c70-8b55-5b65796c36c4 · outbound

This paper cites DIRE for diffusion-generated image detection.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples DIRE for diffusion-generated image detection

Reference 30

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Observation b34ddd3d-5041-497f-8380-2176dd180b3f · outbound

This paper cites Detecting origin attribution for text-to-image diffu- sion models.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Detecting origin attribution for text-to-image diffu- sion models

Reference 32

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Observation 55ecb388-7072-46ca-886a-9cf0706d036f · outbound

This paper cites Deepfake network architecture attribution.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Deepfake network architecture attribution

Reference 33

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Observation 38b730a6-3a2d-4262-bcbd-b2346e28a5a3 · outbound

This paper cites LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 34

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Observation 3ead3ad5-d6bb-458b-88c5-e19e3dfb82e9 · outbound

This paper cites PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection

Reference 35

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Observation 5b36a919-15d9-4440-8f65-f21816a17f50 · outbound

This paper cites For instance, the widely-used GenImage dataset (Zhu et al.,.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples For instance, the widely-used GenImage dataset (Zhu et al.,

Reference 36

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Observation 32f76acb-d5dd-494d-a9aa-461807862318 · outbound

This paper cites This structural homogeneity results in remarkably similar feature distributions among their gener- ated images.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples This structural homogeneity results in remarkably similar feature distributions among their gener- ated images

Reference 37

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Observation 45603e14-ae4d-4c58-9401-9451bd2a372c · outbound

This paper cites Therefore, merely making minor modifications to the weights or model architecture is insufficient to enable the model to express fundamentally different representations.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Therefore, merely making minor modifications to the weights or model architecture is insufficient to enable the model to express fundamentally different representations

Reference 38

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Observation 578002dc-cf05-4244-95c5-2f74cd3b3e9f · outbound

This paper cites As diffusion mod- els (Ho et al.,.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples As diffusion mod- els (Ho et al.,

Reference 39

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This paper cites However, these datasets only cover a limited number of generators, restricting the generalization capability of detectors.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples However, these datasets only cover a limited number of generators, restricting the generalization capability of detectors

Reference 40

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Observation dff1aadc-11f8-4d65-9ad8-7c9b6d6140a8 · outbound

This paper cites Community Forensics (Park & Owens,.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Community Forensics (Park & Owens,

Reference 41

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Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Unresolved cited work

Reference 42

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Observation 03fea5f2-a5b1-4e18-9ecf-05fdadad9e49 · outbound

This paper cites Recent studies (Cocchi et al.,.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Recent studies (Cocchi et al.,

Reference 43

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Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Bi et al

Reference 44

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This paper cites Inspired by these advances, we aim to fully explore the potential of metric learning for out-of-distribution deepfake detection.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Inspired by these advances, we aim to fully explore the potential of metric learning for out-of-distribution deepfake detection

Reference 45

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This paper cites Their motivations are based on the observation that models with different architectures exhibit distinct fingerprints.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Their motivations are based on the observation that models with different architectures exhibit distinct fingerprints

Reference 46

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Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Unresolved cited work

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This paper cites Our synthetic categories include models from the same family, but we rigorously ensure that they are not derived from the same backbone.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Our synthetic categories include models from the same family, but we rigorously ensure that they are not derived from the same backbone

Reference 48

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Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Unresolved cited work

Reference 49

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This paper cites However, this practice can introduce significant real biases by limiting the diversity and representativeness of the authentic class.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples However, this practice can introduce significant real biases by limiting the diversity and representativeness of the authentic class

Reference 50

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Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Unresolved cited work

Reference 51

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This paper cites These are balanced with carefully constructed datasets including Ima- geNet (Russakovsky et al., 2015), MSCOCO (Lin et al.,.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples These are balanced with carefully constructed datasets including Ima- geNet (Russakovsky et al., 2015), MSCOCO (Lin et al.,

Reference 52

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This paper cites This approach not only maximizes the utilization of limited data but also guarantees that model performance is evaluated across diverse and representative subsets.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples This approach not only maximizes the utilization of limited data but also guarantees that model performance is evaluated across diverse and representative subsets

Reference 53

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Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Unresolved cited work

Reference 54

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Observation 80b97ac5-a694-4d47-ade6-7f72b0434c52 · outbound

This paper cites Fake or jpeg? revealing common biases in generated image detection datasets.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Fake or jpeg? revealing common biases in generated image detection datasets

Reference 2014

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Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples OmniGen2: Towards Instruction-Aligned Multimodal Generation

Reference 2016

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Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Supervised contrastive learning

Reference 2018

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Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Improving synthetic image detection towards generalization: An image transformation perspective

Reference 2020

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This paper cites Towards universal fake image detectors that generalize across generative models.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Towards universal fake image detectors that generalize across generative models

Reference 2021

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This paper cites Conceptual 12m: Pushing web- scale image-text pre-training to recognize long-tail visual concepts.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Conceptual 12m: Pushing web- scale image-text pre-training to recognize long-tail visual concepts

Reference 2022

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This paper cites Leveraging frequency analysis for deep fake image recognition.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples Leveraging frequency analysis for deep fake image recognition

Reference 2023

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Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset

Reference 2024

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This paper cites ImagiNet: A Multi-Content Benchmark for Synthetic Image Detection.

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples ImagiNet: A Multi-Content Benchmark for Synthetic Image Detection

Reference 2025

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Pith citing papers

Observation 7db0886d-ed1d-48c7-9ab6-d1135ec15d00 · inbound

ImageAttributionBench: How Far Are We from Generalizable Attribution? cites this paper.

ImageAttributionBench: How Far Are We from Generalizable Attribution? Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples

Reference 73

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LaP-Forensics: Latent-Pixel Consistency Guided Multimodal Reasoning for Deepfake Detection cites this paper.

LaP-Forensics: Latent-Pixel Consistency Guided Multimodal Reasoning for Deepfake Detection Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples

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