Paper Citation Record · LEDGER

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection

As of 17 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2506.02857.

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

pith.paper-citation-record.v1
2506.02857 v1

Coverage vector

measured 41 of 41 reference resolution

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 7ba73cb0-2136-44fe-9d6f-66b3ff0aab44 · outbound

This paper cites Cont inuous fake media detection: Adapting deepfake detectors to new generative techniques.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Cont inuous fake media detection: Adapting deepfake detectors to new generative techniques

Reference 1

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Observation 1d40ed13-1d4d-4ab5-b92b-200bfe9b0068 · outbound

This paper cites Deep learning for multimedia forensics.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Deep learning for multimedia forensics

Reference 2

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Observation a1bccc0e-a87c-4f2c-b3f1-352cad12deb7 · outbound

This paper cites Synthetic Image Verification in the Era of Generative AI: What Works and What Isn't There Yet.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Synthetic Image Verification in the Era of Generative AI: What Works and What Isn't There Yet

Reference 3

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Observation 6600f809-69fe-4841-9bfa-826720c214ed · outbound

This paper cites Detecting GAN-generated Imagery using Color Cues.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Detecting GAN-generated Imagery using Color Cues

Reference 4

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Observation b99fb0a8-d59e-4c3f-a7f2-4526874b71aa · outbound

This paper cites Attributing fake images to gans: Learning and analyzing gan ﬁnger- prints.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Attributing fake images to gans: Learning and analyzing gan ﬁnger- prints

Reference 5

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Observation 73014ac1-dd99-4236-b9c9-1e92a1eb0037 · outbound

This paper cites Do gans leave artiﬁcial ﬁngerprints? In 2019 IEEE conference on multimedia information processing and retrieval (MIPR) , pages 506–511.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Do gans leave artiﬁcial ﬁngerprints? In 2019 IEEE conference on multimedia information processing and retrieval (MIPR) , pages 506–511

Reference 6

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Observation bbf15f53-3398-413c-a229-43372b3aed1e · outbound

This paper cites Deep learning based one-class detection system for fake faces generated by gan network.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Deep learning based one-class detection system for fake faces generated by gan network

Reference 7

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Observation 5a6e1542-8ae5-4784-abb0-03f581c5cedb · outbound

This paper cites FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces

Reference 8

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This paper cites Multimodal forgery detection using ensemble learning.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Multimodal forgery detection using ensemble learning

Reference 9

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Observation 5cb61392-1738-4e72-90b2-5f9be5735643 · outbound

This paper cites Not made for each other-audio-visual dissonance-based deepfake detection and localization.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Not made for each other-audio-visual dissonance-based deepfake detection and localization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:20:53.380773Z

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Observation 7a80b762-2dc2-451a-837b-3cb86bec70cb · outbound

This paper cites Emotions don’t lie: An audio-visual deepfake detection method using affective cues.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Emotions don’t lie: An audio-visual deepfake detection method using affective cues

Reference 11

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Observation 5d96461a-b3d5-486a-b49f-6cfe344fc8ed · outbound

This paper cites Generalized Out-of-Distribution Detection: A Survey.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Generalized Out-of-Distribution Detection: A Survey

Reference 12

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This paper cites Multimodal forgery detection using ensemble learning.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Multimodal forgery detection using ensemble learning

Reference 13

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Observation d865bbfa-f7b3-4619-a7ba-516adb2d71a0 · outbound

This paper cites Fakelocator: Robust localization of gan-based face manipulations.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Fakelocator: Robust localization of gan-based face manipulations

Reference 14

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Observation c5137cc2-9ad7-4bcd-8a62-1c134132c3da · outbound

This paper cites Deepfake detection by analyzing convolutional traces.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Deepfake detection by analyzing convolutional traces

Reference 15

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Observation 8f6f3e83-d12f-4c01-a520-5e311357ca98 · outbound

This paper cites DeepFake Detection Based on the Discrepancy Between the Face and its Context.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection DeepFake Detection Based on the Discrepancy Between the Face and its Context

Reference 16

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Observation d10221b5-d7cb-446c-9c54-a0daa77b0316 · outbound

This paper cites Deepfake Video Detection Using Convolutional Vision Transformer.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Deepfake Video Detection Using Convolutional Vision Transformer

Reference 17

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This paper cites A siamese-based veriﬁcation system for open-set architecture attribution of synthetic images.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection A siamese-based veriﬁcation system for open-set architecture attribution of synthetic images

Reference 18

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This paper cites Syn- thetic image veriﬁcation in the era of generative artiﬁcial intelligence: What works and what isn’t there yet.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Syn- thetic image veriﬁcation in the era of generative artiﬁcial intelligence: What works and what isn’t there yet

Reference 19

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Observation eb150f31-0d10-45a4-98ad-60eeabd7ba54 · outbound

This paper cites Reliable out-of-dis tribution recognition of synthetic images.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Reliable out-of-dis tribution recognition of synthetic images

Reference 20

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Observation dce63e4c-8267-4e0b-8e11-54533db9e5d4 · outbound

This paper cites Improving gener alization of deepfake detection with data farming and few-shot learning.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Improving gener alization of deepfake detection with data farming and few-shot learning

Reference 21

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Observation cc66994b-b05c-45c8-a5d6-881697185f3f · outbound

This paper cites Generalized Zero and Few-Shot Transfer for Facial Forgery Detection.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Generalized Zero and Few-Shot Transfer for Facial Forgery Detection

Reference 22

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This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 23

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This paper cites Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

Reference 24

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This paper cites Learning Confidence for Out-of-Distribution Detection in Neural Networks.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Learning Confidence for Out-of-Distribution Detection in Neural Networks

Reference 25

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This paper cites Can multi-label classiﬁcation networks know what they don’t know? Advances in Neural Information Processing Systems , 34:29074–29087, 2021.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Can multi-label classiﬁcation networks know what they don’t know? Advances in Neural Information Processing Systems , 34:29074–29087, 2021

Reference 26

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Observation 8de5a0bb-9057-4f7c-b061-82cb64e114a6 · outbound

This paper cites En ergy-based out-of-distribution detection.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection En ergy-based out-of-distribution detection

Reference 27

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This paper cites Leveraging visual attention for out-of-distribution detection.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Leveraging visual attention for out-of-distribution detection

Reference 28

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This paper cites Cutmix: Regularization strategy to train strong classiﬁers with lo calizable features.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Cutmix: Regularization strategy to train strong classiﬁers with lo calizable features

Reference 29

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This paper cites Detecting o ut-of-distribution examples with in-distribution ex- amples and gram matrices.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Detecting o ut-of-distribution examples with in-distribution ex- amples and gram matrices

Reference 30

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Observation a3eab43c-73c4-4c89-bfea-974f02cbe805 · outbound

This paper cites Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data

Reference 31

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This paper cites A sim ple uniﬁed framework for detecting out-of- distribution samples and adversarial attacks.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection A sim ple uniﬁed framework for detecting out-of- distribution samples and adversarial attacks

Reference 32

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Observation 681a2c14-67a8-4e12-9ca2-fed902dd5780 · outbound

This paper cites Mixture densities, maximum likelihood and the em algorithm.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Mixture densities, maximum likelihood and the em algorithm

Reference 33

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This paper cites Bayesian learning for neural networks , volume 118.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Bayesian learning for neural networks , volume 118

Reference 34

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This paper cites Markov chain Monte Carlo: stochastic simulation for Bayesian inference.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Markov chain Monte Carlo: stochastic simulation for Bayesian inference

Reference 35

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This paper cites Mixture outlier exposure: Towards out-of-distribution detection in ﬁne-grained env ironments.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Mixture outlier exposure: Towards out-of-distribution detection in ﬁne-grained env ironments

Reference 36

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This paper cites U- net: Convolutional networks for biomedical image seg- mentation.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection U- net: Convolutional networks for biomedical image seg- mentation

Reference 37

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This paper cites Randaugment: Practical automated data augmen- tation with a reduced search space.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Randaugment: Practical automated data augmen- tation with a reduced search space

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:20:52.439428Z

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This paper cites Learning representations by back-propagating errors.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Learning representations by back-propagating errors

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:20:52.415440Z

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This paper cites A continual deepfake detection benchmark: Datase t, methods, and essentials.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection A continual deepfake detection benchmark: Datase t, methods, and essentials

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:20:52.381993Z

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Observation 470e676c-4d46-4d68-88e7-1e4bd40ed528 · outbound

This paper cites Openood v1.5: Enhanced benchmark for out-of-distribution detection, 2023.

Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection Openood v1.5: Enhanced benchmark for out-of-distribution detection, 2023

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:20:52.345750Z

Source-reported events for the cited work

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Pith citing papers

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