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Paper Citation Record · LEDGER
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models
As of 21 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 7 inbound Pith citation observations for arXiv:2411.19117.
A citation records a reference. It does not transfer a finding from one paper to another.
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Outbound references
Observation eb259aad-27d8-4409-bf3e-08caba39b733 · outbound
This paper cites High-resolution image synthesis with latent diffusion models.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models High-resolution image synthesis with latent diffusion models
Reference 1
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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models LoRA: Low-Rank Adaptation of Large Language Models
Reference 2
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This paper cites Representative forgery mining for fake face detection.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Representative forgery mining for fake face detection
Reference 3
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This paper cites Transcending forgery specificity with latent space augmentation for generalizable deepfake detection.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Transcending forgery specificity with latent space augmentation for generalizable deepfake detection
Reference 4
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This paper cites Wavelet-packets for deepfake image analysis and de- tection.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Wavelet-packets for deepfake image analysis and de- tection
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This paper cites Ai-generated image detection using a cross- attention enhanced dual-stream network.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Ai-generated image detection using a cross- attention enhanced dual-stream network
Reference 6
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This paper cites Aer- oblade: Training-free detection of latent diffusion images using autoencoder reconstruction error.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Aer- oblade: Training-free detection of latent diffusion images using autoencoder reconstruction error
Reference 7
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This paper cites RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models DINOv2: Learning Robust Visual Features without Supervision
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This paper cites Deep residual learning for image recognition.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Deep residual learning for image recognition
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This paper cites Learning transferable visual models from natural language supervi- sion.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Learning transferable visual models from natural language supervi- sion
Reference 11
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This paper cites DF40: Toward Next-Generation Deepfake Detection.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models DF40: Toward Next-Generation Deepfake Detection
Reference 12
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This paper cites A style-based generator architecture for generative adversarial networks.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models A style-based generator architecture for generative adversarial networks
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This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Large Scale GAN Training for High Fidelity Natural Image Synthesis
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This paper cites Taming transformers for high-resolution image synthesis.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Taming transformers for high-resolution image synthesis
Reference 15
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Denoising dif- fusion probabilistic models
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Denoising Diffusion Implicit Models
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Scalable diffusion models with transformers
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This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Diffusion models beat gans on image synthesis
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Vector quantized diffusion model for text-to-image synthe- sis
Reference 22
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models https://www.midjourney.com., 2022
Reference 23
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models https://blackforestlabs.ai/announcing- black-forest-labs., 2024
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Leveraging fre- quency analysis for deep fake image recognition
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models A closer look at fourier spectrum discrepan- cies for cnn-generated images detection
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Cnn-generated images are surprisingly easy to spot
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Progressive Growing of GANs for Improved Quality, Stability, and Variation
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models On the detection of synthetic images generated by diffusion mod- els
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Rethinking the up-sampling op- erations in cnn-based generative network for generalizable deepfake detection
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models A Sanity Check for AI-generated Image Detection
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Zero-shot detection of ai-generated images
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Imagenet: A large-scale hierarchical image database
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Collaborative diffusion for multi-modal face generation and editing
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Genimage: A million-scale benchmark for de- tecting ai-generated image
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models iBOT: Image BERT Pre-Training with Online Tokenizer
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Emerg- ing properties in self-supervised vision transformers
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Nomic embed vision: Expanding the nomic latent space
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Stylegan- xl: Scaling stylegan to large diverse datasets
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Celeb-df: A large-scale challenging dataset for deep- fake forensics
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models A style-based generator architecture for generative adversarial networks
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Large-scale celebfaces attributes (celeba) dataset
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models https://www.heygen.com., 2022
Reference 48
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Alias-free generative adversarial networks
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This paper cites https://www.whichfaceisreal.com., 2019.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models https://www.whichfaceisreal.com., 2019
Reference 51
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This paper cites Designing an encoder for stylegan image manipulation.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Designing an encoder for stylegan image manipulation
Reference 52
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Stargan v2: Diverse image synthesis for multiple domains
Reference 53
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This paper cites Styleclip: Text-driven manipulation of stylegan imagery.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Styleclip: Text-driven manipulation of stylegan imagery
Reference 54
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This paper cites https://xihe.mindspore.cn/modelzoo., 2023.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models https://xihe.mindspore.cn/modelzoo., 2023
Reference 55
- Resolution
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
Reference 56
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This paper cites Faceforen- sics++: Learning to detect manipulated facial images.
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Faceforen- sics++: Learning to detect manipulated facial images
Reference 57
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Residual denoising diffu- sion models
Reference 58
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Reference 59
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Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models Unresolved cited work
Reference 60
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Pith citing papers
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Enhancing Self-Supervised Talking Head Forgery Detection via a Training-Free Dual-System Framework cites this paper.
Enhancing Self-Supervised Talking Head Forgery Detection via a Training-Free Dual-System Framework Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models
Reference 39
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Intermediate Representations are Strong AI-Generated Image Detectors cites this paper.
Intermediate Representations are Strong AI-Generated Image Detectors Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models
Reference 53
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Observation 1e6412db-ba6b-492e-9306-a4da1a9ab413 · inbound
HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection cites this paper.
HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models
Reference 73
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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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DRIFT: From Robustness Gaps to Invariance Manifolds for AI-Generated Image Detection cites this paper.
DRIFT: From Robustness Gaps to Invariance Manifolds for AI-Generated Image Detection Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models
Reference 45
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Observation be25e0b6-4010-4c97-8af2-6c7555abe5d9 · inbound
How Fragile Are Training-Free AI-Generated Image Detectors? A Controlled Audit of Score Direction, Preprocessing, and Compression cites this paper.
How Fragile Are Training-Free AI-Generated Image Detectors? A Controlled Audit of Score Direction, Preprocessing, and Compression Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models
Reference 30
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 2bf48227-4e13-4905-9765-a52dfbe035a7 · inbound
GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors cites this paper.
GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models
Reference 30
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Unavailable: canonical work link unavailable.
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Foundation Models are Implicit Deepfake Detectors cites this paper.
Foundation Models are Implicit Deepfake Detectors Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models
Reference 81
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