Perceptual Classifiers: Detecting Generative Images using Perceptual Features | Paper Citation Record

Observation 12dbf4b6-f67d-4fd4-acd5-cd07d40aeac7 · outbound

This paper cites mindspore.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features mindspore

Reference 1

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Perceptual Classifiers: Detecting Generative Images using Perceptual Features Unresolved cited work

Reference 2

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This paper cites Photo forensics from JPEG dimples.IEEE Workshop on Information Forensics and Se- curity (WIFS), pages 1–6, 2017.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Photo forensics from JPEG dimples.IEEE Workshop on Information Forensics and Se- curity (WIFS), pages 1–6, 2017

Reference 3

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This paper cites ARNIQA: Learning Distortion Mani- fold for Image Quality Assessment.IEEE/CVF Winter Con- ference on Applications of Computer Vision (WACV), pages 188–197, 2024.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features ARNIQA: Learning Distortion Mani- fold for Image Quality Assessment.IEEE/CVF Winter Con- ference on Applications of Computer Vision (WACV), pages 188–197, 2024

Reference 4

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This paper cites Synthbuster: Towards Detection of Diffu- sion Model Generated Images.IEEE Open Journal of Signal Processing, 5:1–9, 2024.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Synthbuster: Towards Detection of Diffu- sion Model Generated Images.IEEE Open Journal of Signal Processing, 5:1–9, 2024

Reference 5

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This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 6

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Observation 9b3f68fe-da2f-4b75-8c51-8f6c9c6b4084 · outbound

This paper cites What makes fake images detectable? Understanding prop- erties that generalize.European Conference on Computer Vision, 2020.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features What makes fake images detectable? Understanding prop- erties that generalize.European Conference on Computer Vision, 2020

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.736992Z

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Observation 820cdeea-86e8-419d-b307-3011de802620 · outbound

This paper cites DRCT: Diffusion Reconstruction Contrastive Training to- wards Universal Detection of Diffusion Generated Images.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features DRCT: Diffusion Reconstruction Contrastive Training to- wards Universal Detection of Diffusion Generated Images

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.727947Z

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Observation cdc19085-4dd6-4392-8806-ec8d2e0ce345 · outbound

This paper cites A Single Simple Patch is All You Need for AI-generated Image Detection.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features A Single Simple Patch is All You Need for AI-generated Image Detection

Reference 9

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This paper cites A Bayesian-MRF approach for PRNU- based image forgery detection.IEEE Transactions on In- formation Forensics and Security, 9(4):554–567, 2014.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features A Bayesian-MRF approach for PRNU- based image forgery detection.IEEE Transactions on In- formation Forensics and Security, 9(4):554–567, 2014

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.718703Z

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Observation b7de3600-0825-4a82-99ba-a3b0b445d3a8 · outbound

This paper cites On the de- tection of synthetic images generated by diffusion models,.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features On the de- tection of synthetic images generated by diffusion models,

Reference 11

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Perceptual Classifiers: Detecting Generative Images using Perceptual Features Unresolved cited work

Reference 12

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This paper cites ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection

Reference 13

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This paper cites ImageNet: A large-scale hierarchical im- age database.2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255, 2009.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features ImageNet: A large-scale hierarchical im- age database.2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255, 2009

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.692501Z

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Observation 9d5f2f88-ab95-4dc1-b1e2-9bb01c7bc33c · outbound

This paper cites Diffusion Models Beat GANs on Image Synthesis.Advances in Neural Information Processing Systems, pages 8780–8794, 2021.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Diffusion Models Beat GANs on Image Synthesis.Advances in Neural Information Processing Systems, pages 8780–8794, 2021

Reference 15

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This paper cites Leveraging Fre- quency Analysis for Deep Fake Image Recognition.Interna- tional Conference on Machine Learning, pages 3247–3258,.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Leveraging Fre- quency Analysis for Deep Fake Image Recognition.Interna- tional Conference on Machine Learning, pages 3247–3258,

Reference 16

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This paper cites Alireza Golestaneh, Saba Dadsetan, and Kris M.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Alireza Golestaneh, Saba Dadsetan, and Kris M

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.666475Z

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Observation e1d7eed2-bf46-44bc-a701-86eacd93bf65 · outbound

This paper cites Generative Adversarial Networks.Advances in Neural Information Processing Systems, 27, 2014.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Generative Adversarial Networks.Advances in Neural Information Processing Systems, 27, 2014

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.657811Z

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Observation 8baf45c6-1640-452a-9327-e410c3d4cfcb · outbound

This paper cites Attributing and Detecting Fake Images Generated by Known GANs.2020 IEEE Secu- rity and Privacy Workshops, SP Workshops, San Francisco, CA, USA, May 21, 2020, pages 8–14, 2020.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Attributing and Detecting Fake Images Generated by Known GANs.2020 IEEE Secu- rity and Privacy Workshops, SP Workshops, San Francisco, CA, USA, May 21, 2020, pages 8–14, 2020

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Observation 7b49f310-990d-4260-8f04-02fdea06a21d · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Progressive Growing of GANs for Improved Quality, Stability, and Variation

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Observation 8dcd8260-21f8-46b0-a5a2-46e4275784a2 · outbound

This paper cites A Style- Based Generator Architecture for Generative Adversarial Networks.IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4401–4410, 2019.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features A Style- Based Generator Architecture for Generative Adversarial Networks.IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4401–4410, 2019

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Observation 0066f7d6-576f-4ae5-a394-648c17b9d764 · outbound

This paper cites Musiq: Multi-scale image quality transformer.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Musiq: Multi-scale image quality transformer

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.631406Z

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Observation 59f322c8-ea65-4bce-b6ee-d0008529ae37 · outbound

This paper cites Fully deep blind image quality predictor.IEEE Journal of selected Topics in Signal Processing, 11(1):206–220, 2016.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Fully deep blind image quality predictor.IEEE Journal of selected Topics in Signal Processing, 11(1):206–220, 2016

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.623018Z

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This paper cites Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.613919Z

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Observation b9c01e74-91fc-477a-b179-e028b413e964 · outbound

This paper cites Global Texture Enhancement for Fake Face Detection in the Wild.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Global Texture Enhancement for Fake Face Detection in the Wild

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Observation 4aa044ea-1be5-4b38-b950-a7598b50649f · outbound

This paper cites Global Texture Enhancement for Fake Face Detection in the Wild.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Global Texture Enhancement for Fake Face Detection in the Wild

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Observation 5df2b73c-a03d-489a-b831-5ee42401221e · outbound

This paper cites Image Quality Assessment using Contrastive Learning.IEEE Transactions on Image Processing, 31:4149–4161, 2022.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Image Quality Assessment using Contrastive Learning.IEEE Transactions on Image Processing, 31:4149–4161, 2022

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Observation ce9b31fe-2227-407d-aeb6-7826df4e3a4e · outbound

This paper cites Do GANs leave artificial fingerprints? 2019 IEEE conference on multimedia information process- ing and retrieval (MIPR), pages 506–511, 2019.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Do GANs leave artificial fingerprints? 2019 IEEE conference on multimedia information process- ing and retrieval (MIPR), pages 506–511, 2019

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Observation d4109a70-12b2-44e3-b428-0a240e92c9a9 · outbound

This paper cites No-Reference Image Quality Assessment in the Spa- tial Domain.IEEE Transactions on Image Processing, 21 (12):4695–4708, 2012.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features No-Reference Image Quality Assessment in the Spa- tial Domain.IEEE Transactions on Image Processing, 21 (12):4695–4708, 2012

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This paper cites Completely Blind.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Completely Blind

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Observation b38dda3e-5914-4464-8a4e-f43352adfbd0 · outbound

This paper cites Blind Im- age Quality Assessment: From Natural Scene Statistics to Perceptual Quality .IEEE Transactions on Image Process- ing, 20(12):3350–3364, 2011.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Blind Im- age Quality Assessment: From Natural Scene Statistics to Perceptual Quality .IEEE Transactions on Image Process- ing, 20(12):3350–3364, 2011

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This paper cites Detecting GAN generated Fake Images using Co-occurrence Matrices.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Detecting GAN generated Fake Images using Co-occurrence Matrices

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Observation 08f90893-d161-461c-9d78-47bf641729dc · outbound

This paper cites Bappy, Amit K.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Bappy, Amit K

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Observation a43c09a4-e15f-4480-9f55-afcf6a49c065 · outbound

This paper cites Toward a Practical Perceptual Video Quality Metric.https://netflixtechblog.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Toward a Practical Perceptual Video Quality Metric.https://netflixtechblog

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Perceptual Classifiers: Detecting Generative Images using Perceptual Features Unresolved cited work

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This paper cites Exposing photo manipu- lation with inconsistent reflections.ACM Trans.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Exposing photo manipu- lation with inconsistent reflections.ACM Trans

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Perceptual Classifiers: Detecting Generative Images using Perceptual Features Unresolved cited work

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Observation 769e66a2-f47e-4432-a4d7-8f21403f5c6d · outbound

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Perceptual Classifiers: Detecting Generative Images using Perceptual Features Semantic Image Synthesis with Spatially-Adaptive Normalization.IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2337–2346, 2019

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Observation 058540d2-2b46-40fb-bc62-9cf320275260 · outbound

This paper cites Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues .European Conference on Computer Vision, pages 86–103, 2020.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues .European Conference on Computer Vision, pages 86–103, 2020

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Observation 6884e85b-8a70-4d02-8ff7-16cfc050eae6 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.International Conference on Machine Learning, 139:8748–8763, 2021.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Learning Transferable Visual Models From Natural Language Supervision.International Conference on Machine Learning, 139:8748–8763, 2021

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Observation e1998ec1-e614-4377-a99b-7f14373150fa · outbound

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Perceptual Classifiers: Detecting Generative Images using Perceptual Features Zero-Shot Text-to-Image Generation, 2021

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This paper cites Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer

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Observation fcaa790d-f871-463d-881e-4c7fefa6f9f2 · outbound

This paper cites Saad and Alan C.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Saad and Alan C

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Observation 991d570a-fd11-4bca-b50f-d4b904724ced · outbound

This paper cites Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild.IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 5846–5855, 2023.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild.IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 5846–5855, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.449603Z

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This paper cites LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

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Perceptual Classifiers: Detecting Generative Images using Perceptual Features Unresolved cited work

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Observation ee2d86f7-257c-4bdf-865b-4fba4a3eb2ae · outbound

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

Perceptual Classifiers: Detecting Generative Images using Perceptual Features LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

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Perceptual Classifiers: Detecting Generative Images using Perceptual Features Unresolved cited work

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Observation 4d70b48b-91ca-4c51-a67e-5e24f4ce0c21 · outbound

This paper cites RAPIQUE: Rapid and accurate video quality prediction of user generated content.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features RAPIQUE: Rapid and accurate video quality prediction of user generated content

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.422526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a453e41d-2d19-44c2-92f9-84a5e85bf03d · outbound

This paper cites Maxvit: Multi-axis vision transformer.European Conference on Computer Vision, pages 459–479, 2022.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Maxvit: Multi-axis vision transformer.European Conference on Computer Vision, pages 459–479, 2022

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.412798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5c78e951-3f1c-424b-84cc-2d6c7a90e409 · outbound

This paper cites CNN-generated images are surprisingly easy to spot.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features CNN-generated images are surprisingly easy to spot

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.404055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1863aff6-e049-43d4-a771-d45dddeace09 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.IEEE Transactions on Image Process- ing, 13(4):600–612, 2004.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Image quality assessment: from error visibility to structural similarity.IEEE Transactions on Image Process- ing, 13(4):600–612, 2004

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.394810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4dcc1c11-54dc-45db-ba9b-774431a1f238 · outbound

This paper cites DIRE for Diffusion-Generated Image Detection.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features DIRE for Diffusion-Generated Image Detection

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T14:57:49.195607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edcd21a9-f1a8-42b7-8acf-30f20f0ab436 · outbound

This paper cites Detecting fake images by identifying potential texture difference.Future Gener.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Detecting fake images by identifying potential texture difference.Future Gener

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.386052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 25a99e65-2298-4da0-959e-76d2f6da8030 · outbound

This paper cites From Patches to Pic- tures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality .IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features From Patches to Pic- tures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality .IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.376575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b4c09bc6-e45f-42ed-a31a-49509b25c2df · outbound

This paper cites Attributing Fake Images to GANs: Learning and Analyzing GAN Finger- prints.IEEE/CVF International Conference on Computer Vision, pages 7556–7566, 2019.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Attributing Fake Images to GANs: Learning and Analyzing GAN Finger- prints.IEEE/CVF International Conference on Computer Vision, pages 7556–7566, 2019

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.366642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e39cdd99-b9a1-473e-9656-b95c0fb48b52 · outbound

This paper cites A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T14:57:49.207150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41c3c959-796c-44d0-a8ec-58f7a30d62d7 · outbound

This paper cites Blind Image Quality Assessment Using a Deep Bi- linear Convolutional Neural Network .IEEE Transactions on Circuits and Systems for Video Technology, 30(1):36–47,.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Blind Image Quality Assessment Using a Deep Bi- linear Convolutional Neural Network .IEEE Transactions on Circuits and Systems for Video Technology, 30(1):36–47,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.357471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 09ba1c2a-3692-4b35-9e61-f8c9045f7428 · outbound

This paper cites an unresolved cited work.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:57:49.348519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3f384d59-3f5c-44fd-b31e-18d0f8d5ffb8 · outbound

This paper cites Detecting and Simulating Artifacts in GAN Fake Images.IEEE In- ternational Workshop on Information Forensics and Security (WIFS), pages 1–6, 2019.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features Detecting and Simulating Artifacts in GAN Fake Images.IEEE In- ternational Workshop on Information Forensics and Security (WIFS), pages 1–6, 2019

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.339628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b2e5f181-a419-40ab-9710-efd1288de93e · outbound

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

Perceptual Classifiers: Detecting Generative Images using Perceptual Features PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T14:57:49.219284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46e469a1-72eb-49a5-bf29-af67ea00baec · outbound

This paper cites LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.Ad- vances in Neural Information Processing Systems, 36, 2024.

Perceptual Classifiers: Detecting Generative Images using Perceptual Features LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.Ad- vances in Neural Information Processing Systems, 36, 2024

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:49.330235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Outbound references

Pith citing papers