Paper Citation Record · LEDGER

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection

As of 9 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2511.18436.

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

pith.paper-citation-record.v1
2511.18436 v2

Coverage vector

measured 57 of 57 reference resolution

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measured 58 of 58 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T17:35:40.287493Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

57 of 57 outbound references displayed

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External citation measurements

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Outbound references

Observation fa6e9839-c764-4c85-ac84-482a6a72d5b0 · outbound

This paper cites Memory aware synapses: Learning what (not) to forget.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Memory aware synapses: Learning what (not) to forget

Reference 1

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Observation 51dde521-6cff-4bbd-adde-546234b75c4b · outbound

This paper cites Albumentations: fast and flexible image augmenta- tions.Information, 11(2):125, 2020.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Albumentations: fast and flexible image augmenta- tions.Information, 11(2):125, 2020

Reference 2

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Observation 200b8ccf-fcf1-4400-a382-c9e6b93f8ff9 · outbound

This paper cites End-to-end reconstruction- classification learning for face forgery detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection End-to-end reconstruction- classification learning for face forgery detection

Reference 3

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Observation 762c0f6c-2323-4658-b783-8f2d38a2a5e2 · outbound

This paper cites Self-supervised learning of adversarial exam- ple: Towards good generalizations for deepfake detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Self-supervised learning of adversarial exam- ple: Towards good generalizations for deepfake detection

Reference 4

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Observation 8ec76af8-787e-4e70-8159-4022894473ba · outbound

This paper cites Local relation learning for face forgery detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Local relation learning for face forgery detection

Reference 5

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Observation 688137ec-cf8d-4750-bdaa-58de47a7c873 · outbound

This paper cites DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis

Reference 6

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Observation b770f75e-b22a-4374-ae01-4d2fcde03550 · outbound

This paper cites Can we leave deepfake data behind in training deepfake detector?Advances in Neural Information Processing Systems, 37:21979–21998, 2024.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Can we leave deepfake data behind in training deepfake detector?Advances in Neural Information Processing Systems, 37:21979–21998, 2024

Reference 7

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Observation 1143e57e-f7cb-4fa7-ae99-c0a63c8f6381 · outbound

This paper cites Stacking brick by brick: Aligned feature isolation for incremental face forgery detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Stacking brick by brick: Aligned feature isolation for incremental face forgery detection

Reference 8

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Observation c04bb9b2-3316-41ff-91d4-8192e419f428 · outbound

This paper cites Ed ˆ4: Explicit data- level debiasing for deepfake detection.IEEE Transactions on Image Processing, 2025.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Ed ˆ4: Explicit data- level debiasing for deepfake detection.IEEE Transactions on Image Processing, 2025

Reference 9

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Observation b90ecb7d-41f7-4aa4-b701-28535064c640 · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Xception: Deep learning with depthwise separable convolutions

Reference 10

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Observation 8597d7d3-77d9-46b3-9863-7c1c9b78d234 · outbound

This paper cites Forensics adapter: Adapting clip for generalizable face forgery detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Forensics adapter: Adapting clip for generalizable face forgery detection

Reference 11

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Observation 9efb05ba-53ae-4888-b80d-747e41b34ad1 · outbound

This paper cites A continual learning survey: Defying for- getting in classification tasks.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(7):3366–3385, 2021.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection A continual learning survey: Defying for- getting in classification tasks.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(7):3366–3385, 2021

Reference 12

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Observation a5db47f6-6b4c-4195-9330-317d1ee4d2f9 · outbound

This paper cites com / c / deepfake - detection - challengeAc- cessed 2021-04-24.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection com / c / deepfake - detection - challengeAc- cessed 2021-04-24

Reference 13

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Observation 48743804-3f1a-4a32-8606-353d6d58b9f4 · outbound

This paper cites Implicit identity leakage: The stum- bling block to improving deepfake detection generalization.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Implicit identity leakage: The stum- bling block to improving deepfake detection generalization

Reference 14

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Observation 2321a890-1d41-41a8-9395-4b8316b0865c · outbound

This paper cites Ddgr: Continual learning with deep diffusion-based generative replay.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Ddgr: Continual learning with deep diffusion-based generative replay

Reference 15

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Observation eeeb0598-0056-493c-a33e-8a1bf0695b21 · outbound

This paper cites Learning meta face recognition in un- seen domains.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Learning meta face recognition in un- seen domains

Reference 16

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Observation c9564e0d-bd7b-4c0f-ac13-1c4a3c91f5dc · outbound

This paper cites Deep residual learning for image recognition.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Deep residual learning for image recognition

Reference 17

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Observation cc314ef2-f249-42c1-8556-22575ba4c605 · outbound

This paper cites Denoising diffu- sion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851, 2020.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Denoising diffu- sion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851, 2020

Reference 18

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Observation 775b60cf-38d2-444d-825a-f2cdfdef70fe · outbound

This paper cites Implicit identity driven deepfake face swapping detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Implicit identity driven deepfake face swapping detection

Reference 19

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Observation a75922fb-e86e-4a75-a296-dfbbacbd8d03 · outbound

This paper cites Freqdebias: Towards generalizable deepfake detec- tion via consistency-driven frequency debiasing.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Freqdebias: Towards generalizable deepfake detec- tion via consistency-driven frequency debiasing

Reference 20

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Observation 69b8b7db-e96a-44d5-989b-fdac71f6cda2 · outbound

This paper cites Sddgr: Stable diffusion-based deep generative replay for class incremental object detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Sddgr: Stable diffusion-based deep generative replay for class incremental object detection

Reference 21

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Observation e24dcd27-290f-43de-b23f-963beae9a2b5 · outbound

This paper cites Cored: Gen- eralizing fake media detection with continual representation using distillation.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Cored: Gen- eralizing fake media detection with continual representation using distillation

Reference 22

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Observation 02716608-e80f-49c4-bac9-becde9f0b199 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Adam: A Method for Stochastic Optimization

Reference 23

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Observation 2f670f4b-f75a-4926-8e28-80fbc31de675 · outbound

This paper cites Overcoming catastrophic forgetting in neu- ral networks.Proceedings of the National Academy of Sci- ences, 114(13):3521–3526, 2017.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Overcoming catastrophic forgetting in neu- ral networks.Proceedings of the National Academy of Sci- ences, 114(13):3521–3526, 2017

Reference 24

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Observation 7e5f4968-6c6b-4520-9db9-ca39c2ba3f89 · outbound

This paper cites Face x-ray for more gen- 9 eral face forgery detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Face x-ray for more gen- 9 eral face forgery detection

Reference 25

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Observation 7c73549d-2aed-46fa-9806-c9a2cb31be9e · outbound

This paper cites Celeb-df: A new dataset for deepfake forensics.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Celeb-df: A new dataset for deepfake forensics

Reference 26

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Observation 7fbf4912-da3f-4925-8af9-c4fc93194219 · outbound

This paper cites Learning without forgetting.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Learning without forgetting

Reference 27

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Observation 32296f07-e146-47c3-895a-a78c5895441f · outbound

This paper cites Exploring disentangled content information for face forgery detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Exploring disentangled content information for face forgery detection

Reference 28

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Observation 90cf68e8-bc01-4afa-b62a-6fa80592797f · outbound

This paper cites Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learn- ing.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learn- ing

Reference 29

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Observation 41dfb151-feb7-4a5a-b567-5fc79ebc45d2 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 30

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Observation 418bc983-b2db-474d-807f-c21b68fce7fd · outbound

This paper cites Dfil: Deepfake incremental learning by exploiting domain-invariant forgery clues.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Dfil: Deepfake incremental learning by exploiting domain-invariant forgery clues

Reference 31

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This paper cites Scalable diffusion mod- els with transformers.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Scalable diffusion mod- els with transformers

Reference 32

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Observation eadbf2d7-022e-4439-b62a-e16cddab321b · outbound

This paper cites Thinking in frequency: Face forgery detection by mining frequency-aware clues.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Thinking in frequency: Face forgery detection by mining frequency-aware clues

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When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection icarl: Incremental classifier and representation learning

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When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection High-resolution image synthesis with latent diffusion models

Reference 35

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This paper cites High-resolution image synthesis with latent diffusion models.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection High-resolution image synthesis with latent diffusion models

Reference 36

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This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection U- net: Convolutional networks for biomedical image segmen- tation

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This paper cites Faceforen- sics++: Learning to detect manipulated facial images.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Faceforen- sics++: Learning to detect manipulated facial images

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This paper cites Continual learning with deep generative replay.Advances in Neural Information Processing Systems, 30, 2017.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Continual learning with deep generative replay.Advances in Neural Information Processing Systems, 30, 2017

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This paper cites Detecting deep- fakes with self-blended images.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Detecting deep- fakes with self-blended images

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Observation 2ffaa362-5f86-440b-9cf8-d41fcc9eeefd · outbound

This paper cites Always be dreaming: A new approach for data-free class-incremental learning.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Always be dreaming: A new approach for data-free class-incremental learning

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This paper cites Denoising Diffusion Implicit Models.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Denoising Diffusion Implicit Models

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This paper cites Dual contrastive learning for general face forgery detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Dual contrastive learning for general face forgery detection

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Observation ea7dff67-b09d-4f5d-b92e-c8e3b8418b29 · outbound

This paper cites Continual face forgery detection via historical distribution preserving.International Journal of Computer Vision, 133(3):1067–1084, 2025.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Continual face forgery detection via historical distribution preserving.International Journal of Computer Vision, 133(3):1067–1084, 2025

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This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Efficientnet: Rethinking model scaling for convolutional neural networks

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Observation e03898f5-ed1a-46c1-b5a0-370142707b6d · outbound

This paper cites Dynamic mixed-prototype model for incremental deepfake detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Dynamic mixed-prototype model for incremental deepfake detection

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Observation 1bb19a3b-b928-4366-9d59-88322835a892 · outbound

This paper cites Representative forgery mining for fake face detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Representative forgery mining for fake face detection

Reference 47

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This paper cites Der: Dynam- ically expandable representation for class incremental learn- ing.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Der: Dynam- ically expandable representation for class incremental learn- ing

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Observation 98f4626f-2a91-4b20-a60f-5b0582600a99 · outbound

This paper cites Ucf: Uncovering common features for generalizable deep- fake detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Ucf: Uncovering common features for generalizable deep- fake detection

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Observation a0c555d6-117e-46e1-8dca-c65b07b5b639 · outbound

This paper cites DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection

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Resolution: unresolved; no resolver link, observed 2026-08-03T20:51:00.128393Z

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Observation adc6b0ef-2b33-4515-81b0-9ad597ea1f92 · outbound

This paper cites Transcending forgery specificity with latent space augmentation for generalizable deepfake detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Transcending forgery specificity with latent space augmentation for generalizable deepfake detection

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Observation fee6596b-b236-4905-93d0-902147324ed3 · outbound

This paper cites DF40: Toward Next-Generation Deepfake Detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection DF40: Toward Next-Generation Deepfake Detection

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T20:51:00.320975Z

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Observation 8bf89893-9fe7-4dd0-ad5d-2a42fa7e6d88 · outbound

This paper cites Effort: Efficient orthogonal mod- eling for generalizable ai-generated image detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Effort: Efficient orthogonal mod- eling for generalizable ai-generated image detection

Reference 53

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Observation 71a0e75c-ab3f-4acf-ace3-b14a2782b9b5 · outbound

This paper cites Multi-attentional deep- fake detection.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Multi-attentional deep- fake detection

Reference 54

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Observation 20f2f7bb-5552-434f-bb3d-518e69bf6900 · outbound

This paper cites an unresolved cited work.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection Unresolved cited work

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-03T20:51:00.586365Z

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Observation 99cd97fb-2156-42d6-b03c-4f080f5c16ce · outbound

This paper cites 1, we present qualitative examples of re- play samples generated by our LDM [35] generator.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection 1, we present qualitative examples of re- play samples generated by our LDM [35] generator

Reference 56

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Observation ab3a57aa-e0f5-4b41-83f3-7a23e60ada0f · outbound

This paper cites domain-safe.

When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection domain-safe

Reference 57

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Pith citing papers

Observation 1185fc3e-5e33-4453-b823-bcd08dd42772 · inbound

Pixel-Space Diffusion Transformers cites this paper.

Pixel-Space Diffusion Transformers When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection

Reference 122

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