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Paper Citation Record · LEDGER

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization

As of 10 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 0 inbound Pith citation observations for arXiv:2509.07993.

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

pith.paper-citation-record.v1
2509.07993 v1

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:12:32.909169Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

70 of 70 outbound references displayed

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External citation measurements

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Outbound references

Observation e6184316-4339-41c9-8a93-f82ea25eca36 · outbound

This paper cites Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images

Reference 2

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Observation f71e46f9-808e-4edd-898d-df836e8ef0a6 · outbound

This paper cites Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System

Reference 3

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Observation 87b1fd33-5db2-489c-9a5c-cf6bdf729bb6 · outbound

This paper cites Cifake: Image classifica- tion and explainable identification of ai-generated synthetic images.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Cifake: Image classifica- tion and explainable identification of ai-generated synthetic images

Reference 4

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This paper cites Dark experience for general continual learning: a strong, simple baseline.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Dark experience for general continual learning: a strong, simple baseline

Reference 5

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Observation 67f49e8d-f9c1-4049-bd00-ae9d0a8a2aa9 · outbound

This paper cites Harnessing the power of text-image contrastive models for automatic detection of on- line misinformation.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Harnessing the power of text-image contrastive models for automatic detection of on- line misinformation

Reference 6

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Observation 8fd24f3f-8db1-41c0-a826-813ea4ba2aab · outbound

This paper cites Masked Conditional Diffusion Model for Enhancing Deepfake Detection.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Masked Conditional Diffusion Model for Enhancing Deepfake Detection

Reference 7

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Observation 7bd0f2d1-8e8d-4d89-b428-0f4d25d1a44c · outbound

This paper cites Fakecatcher: Detection of synthetic portrait videos using biological sig- nals.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Fakecatcher: Detection of synthetic portrait videos using biological sig- nals

Reference 8

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Observation 39015386-96f0-48cd-827f-e2f7fbd9f3d9 · outbound

This paper cites Forensictrans- fer: Weakly-supervised domain adaptation for forgery detec- tion.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Forensictrans- fer: Weakly-supervised domain adaptation for forgery detec- tion

Reference 9

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Observation 4585362d-7c27-4eb0-b087-ad2dbe980d62 · outbound

This paper cites On the detection of digital face manipulation.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization On the detection of digital face manipulation

Reference 10

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Observation 1e9feb2c-205d-4adc-a9dd-ebdef4c11773 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 11

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Observation db69f514-3b28-44ff-a547-9fe6d91a77f9 · outbound

This paper cites Attacking identity semantics in deepfakes via deep feature fusion.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Attacking identity semantics in deepfakes via deep feature fusion

Reference 12

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This paper cites Synthesizing black-box anti-forensics deepfakes with high visual quality.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Synthesizing black-box anti-forensics deepfakes with high visual quality

Reference 13

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Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Detection, Attribution and Localization of GAN Generated Images

Reference 14

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Observation 56c3fa0d-91d9-4bc5-824d-670a3057b6fa · outbound

This paper cites An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

Reference 15

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Observation 91809140-fb64-40a5-897a-f22e1521d602 · outbound

This paper cites Studies of mind and brain: Neural principles of learning, perception, development, cognition, and motor control.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Studies of mind and brain: Neural principles of learning, perception, development, cognition, and motor control

Reference 16

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Observation 927bbfc9-ff3d-4ea9-9488-8e630ffc7fbb · outbound

This paper cites Deepfake detection by analyzing convolutional traces.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Deepfake detection by analyzing convolutional traces

Reference 17

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Observation d1a557ab-3a3c-4bac-8bb1-5e4e29f3f29d · outbound

This paper cites Robust attentive deep neural network for detecting gan- generated faces.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Robust attentive deep neural network for detecting gan- generated faces

Reference 18

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This paper cites Deep fake image detection based on pairwise learning.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Deep fake image detection based on pairwise learning

Reference 19

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This paper cites Improving the generalization ability of deepfake detection via disentangled representation learning.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Improving the generalization ability of deepfake detection via disentangled representation learning

Reference 20

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Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization De- tecting cnn-generated facial images in real-world scenarios

Reference 21

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Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Unresolved cited work

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Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Unresolved cited work

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Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Overcoming catastrophic forgetting in neu- ral networks

Reference 24

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This paper cites DeepFakes: a New Threat to Face Recognition? Assessment and Detection.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization DeepFakes: a New Threat to Face Recognition? Assessment and Detection

Reference 25

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This paper cites Towards generalized deepfake detection with continual learning on limited new data.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Towards generalized deepfake detection with continual learning on limited new data

Reference 26

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This paper cites Faster than lies: Real-time deepfake detection using binary neural networks.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Faster than lies: Real-time deepfake detection using binary neural networks

Reference 27

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This paper cites How Generalizable are Deepfake Image Detectors? An Empirical Study.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization How Generalizable are Deepfake Image Detectors? An Empirical Study

Reference 28

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This paper cites A continual deepfake detection benchmark: Dataset, meth- ods, and essentials.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization A continual deepfake detection benchmark: Dataset, meth- ods, and essentials

Reference 29

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Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization In ictu oculi: Exposing ai created fake videos by detecting eye blinking

Reference 30

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Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Celeb-df: A large-scale challenging dataset for deep- fake forensics

Reference 31

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

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Exploring disentangled content information for face forgery detection

Reference 32

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This paper cites Detecting Multimedia Generated by Large AI Models: A Survey.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Detecting Multimedia Generated by Large AI Models: A Survey

Reference 33

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This paper cites Preserving fairness generalization in deepfake detec- tion.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Preserving fairness generalization in deepfake detec- tion

Reference 34

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Observation 04edbe94-2e7c-4a68-9e91-0b9f1c0f5e6d · outbound

This paper cites Global tex- ture enhancement for fake face detection in the wild.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Global tex- ture enhancement for fake face detection in the wild

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-05T14:12:39.414654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:28.816575Z digest=sha256:8037518efcd271592e7b1ac7c62a526c340521b449314473e086bc8c36e53101

Observation d61917a2-fab9-4ce0-9e68-4bf5365e2d01 · outbound

This paper cites Gradient episodic memory for continual learning.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Gradient episodic memory for continual learning

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:39.244407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:28.877058Z digest=sha256:e196b703c4949bbc357d6b51c4f4e4971331504ea5102b905b67c51a0ea592f5

Observation 0f62b40c-fbfe-4e5f-b239-87b668d1f2e9 · outbound

This paper cites Detecting images generated by deep diffusion models using their lo- cal intrinsic dimensionality.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Detecting images generated by deep diffusion models using their lo- cal intrinsic dimensionality

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:39.014779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:28.931810Z digest=sha256:52704136a93303089b83f5ebfc307cd79ec7403063db06f0b23abb4c2d455f7a

Observation 85968d59-d128-4791-a74d-987a6eb5a38d · outbound

This paper cites Incremental learning for the detection and clas- sification of gan-generated images.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Incremental learning for the detection and clas- sification of gan-generated images

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:38.778894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:29.034904Z digest=sha256:470db8e2d6a1d20bdd756836386511e18c6b46aec6beb7239bb74e768f91598c

Observation 301bd7c3-c57e-4ebc-ba5d-1593232bbf9e · outbound

This paper cites Deepfakes genera- tion and detection: State-of-the-art, open challenges, coun- termeasures, and way forward.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Deepfakes genera- tion and detection: State-of-the-art, open challenges, coun- termeasures, and way forward

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:38.604646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:29.096734Z digest=sha256:32ed12a78b969111e9bf4c6a5c3b200148b4751337b493e7f320d7088dabe875

Observation 66a9ed58-e97a-472e-8dac-1ed239a75cb4 · outbound

This paper cites GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-05T14:12:29.226417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:12:29.226417Z digest=sha256:121e5cc4395586eb672ded6f20627c23108d45e8bc8deea9f197a0993f797df0

Observation 132e6df7-9975-49a9-b14b-d709a4cc33e0 · outbound

This paper cites Multi-task learning for detecting and segment- ing manipulated facial images and videos.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Multi-task learning for detecting and segment- ing manipulated facial images and videos

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:38.305770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:29.324504Z digest=sha256:de5af173f1a620a56e050abe58e56e2eeecb3e10088866fc482ee9e5c85f3172

Observation fde01c05-c11d-446d-840a-033443824deb · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-05T14:12:29.417039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:12:29.417039Z digest=sha256:7bc4cb718717cb7a44b55e184539cca27a882ae27d67199b1b52ed073dfa2591

Observation 1c9e0e42-e22f-4156-bfce-bea5056c5c81 · outbound

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

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Dfil: Deepfake incremental learning by exploiting domain-invariant forgery clues

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:37.937304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:29.509985Z digest=sha256:ab9d8135fdc5686770ab2dc081f60e0c43fdde7b7348e4336285032d509e7a69

Observation 127cd40e-5128-4963-a58d-ede83acb2c55 · outbound

This paper cites Learning a deep dual-level network for robust deepfake detection.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Learning a deep dual-level network for robust deepfake detection

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:37.655348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:29.603827Z digest=sha256:c1dc7f17880f762e34e12ca77f3f4c2921023893fdd8e35175f9229bce825770

Observation 1d9f813e-c364-4e7e-a537-081ad00f257b · outbound

This paper cites Mobilenetv4: universal models for the mobile ecosystem.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Mobilenetv4: universal models for the mobile ecosystem

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:37.350343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:29.724525Z digest=sha256:ff95eb162a86901413897c712a19d0c25a226470ad489c2b6fcca9bb6c9a648e

Observation 09e5c09a-f111-4bb7-9a8c-0dd28f626f73 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-05T14:12:29.826405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:12:29.826405Z digest=sha256:bb6f01b3f01cc452afee947871a3cda2b2aac0ebfc32f6dc919f02e49cb5c102

Observation 40f2d71d-1d61-46c5-8a1f-6909817928e5 · outbound

This paper cites ImageNet-21K Pretraining for the Masses.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization ImageNet-21K Pretraining for the Masses

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-05T14:12:29.948773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:12:29.948773Z digest=sha256:ef2e2cee3d967357e46b716a53b05a1ece7f7b547ece0edf1ed74ca046b36e35

Observation ee41bcc1-259b-433a-b2cf-69e99303d36f · outbound

This paper cites Experience replay for continual learning.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Experience replay for continual learning

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:37.217150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:30.055959Z digest=sha256:5f37fe18276116a1ee64ef73979b421ee1063a775f27fc5b26ba30e2e7bbfe9f

Observation 6eeb57f4-15ed-4ef1-bebb-69d276f3e468 · outbound

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

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization High-resolution image synthesis with latent diffusion models

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:37.071014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:30.166115Z digest=sha256:9a8ca3798e6256fe411e2fca6b4509c952ca6edb1c88dcfa155a9a47171173d6

Observation 114039ec-82cf-4a5f-8204-cb7d2f2833da · outbound

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

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Faceforen- sics++: Learning to detect manipulated facial images

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:36.896590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:30.227737Z digest=sha256:6a171a6277386f80befe67d2c8d6fc2bd7abc1939da50fc56ae19af6965c7771

Observation 2788461b-afe7-4f9f-82ef-92c074de5421 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Photorealistic text-to-image diffusion models with deep language understanding

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:36.722304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:30.430587Z digest=sha256:cd724b000429400d6a0ddd1bdee8ad45f10ccae7a0f91f4b4978905bd9151a12

Observation 40806fdb-3cff-4534-8e8e-aa8f49a7cf28 · outbound

This paper cites Error sensitivity modulation based experience replay: Mitigating abrupt representation drift in continual learning.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Error sensitivity modulation based experience replay: Mitigating abrupt representation drift in continual learning

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:36.542597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:30.611992Z digest=sha256:53972b763728f8cc3fbc74d5bf242c36b9a78255aa0f6f4f67e85ddb22a55fb0

Observation 8a18fa8b-8382-474f-9777-dd54caaa9939 · outbound

This paper cites Con- tinuous fake media detection: Adapting deepfake detectors to new generative techniques.Comput.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Con- tinuous fake media detection: Adapting deepfake detectors to new generative techniques.Comput

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:36.399200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:30.789173Z digest=sha256:438a25ea6893f6071ad4e76b0830f43eb057228efbb5212827cf5be42c36f6e5

Observation b2f01995-ce8f-4266-a2bc-171e9bf68be2 · outbound

This paper cites An examination of fairness of ai models for deepfake detection.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization An examination of fairness of ai models for deepfake detection

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:36.224251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:30.961889Z digest=sha256:8e1716f53f967fbb3fc7c582588e8adda787b92065699152c12c3130802d6f1a

Observation 4fdce8f3-4a48-4974-a0d5-f1b04e001aa7 · outbound

This paper cites En- hancing generalization ability in deepfake detection via con- tinual learning.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization En- hancing generalization ability in deepfake detection via con- tinual learning

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:36.045485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:30.997427Z digest=sha256:6089e06c979f12229ebf5f8efd5107c163eed6f84cd14c86cb1c3b86c6c7f5a4

Observation a337adb3-2a39-42bc-a370-f08f22dff0bf · outbound

This paper cites Three scenarios for continual learning.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Three scenarios for continual learning

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-05T14:12:31.009858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:12:31.009858Z digest=sha256:8b81cb3710d34c1ed1b34c64c6854d92b69de4fd9fb399004f7102ac2f904ab7

Observation f56a4823-4d5e-4d28-ae6d-1ea7758719ba · outbound

This paper cites Fastvit: A fast hybrid vision transformer using structural reparameterization.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Fastvit: A fast hybrid vision transformer using structural reparameterization

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:35.898760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:31.119882Z digest=sha256:1d62783f529882f031de6195b31f9cab66bdda0a1c3ce5b554d4563232a33af6

Observation 898775ad-5d33-4de2-b7ce-3a725da5ec28 · outbound

This paper cites Cnn-generated images are sur- prisingly easy to spot.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Cnn-generated images are sur- prisingly easy to spot

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:35.763339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:31.253748Z digest=sha256:cabcf69841000dede69b6454c9ed5590ed596b5f2c555ea0394322db435d640c

Observation 305ea4b2-6abb-4bc6-9bc5-47c78a85d9d4 · outbound

This paper cites Deepfake detectors and datasets exhibit racial and gender bias, usc study shows.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Deepfake detectors and datasets exhibit racial and gender bias, usc study shows

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:35.575903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:31.354516Z digest=sha256:5faa0d77918f854b67bbded71a2e2c29bed612a16b51fb82b4dff4c4a9818ce8

Observation babfd60e-8e06-42a9-ba9a-a2eebe1dfd05 · outbound

This paper cites Con- vnext v2: Co-designing and scaling convnets with masked autoencoders.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Con- vnext v2: Co-designing and scaling convnets with masked autoencoders

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:35.257418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:31.505456Z digest=sha256:598eab61283289403c99e073a7bf57488b57498f8c62cb1842a3af0490df6d8b

Observation 46ca9dcc-d945-46e4-9d1c-d074de562001 · outbound

This paper cites Analyzing Fairness in Deepfake Detection With Massively Annotated Databases.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Analyzing Fairness in Deepfake Detection With Massively Annotated Databases

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-05T14:12:31.623379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:12:31.623379Z digest=sha256:2c777826de4bc81001f0542dd46bb5023f676b6ce5080a4d4234064e012af6dd

Observation 87bb33af-79b3-4fc7-8040-e914a09d8a50 · outbound

This paper cites Yan et al.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Yan et al

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:35.083867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:31.749058Z digest=sha256:d023702020fd69fc4414c0ef15f7dcb1078f39236181dcc1ff07eb3fa4b18356

Observation 8615148c-2404-44fc-ab30-611c30dc34b7 · outbound

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

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Ucf: Uncovering common features for generalizable deep- fake detection

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:34.962084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:31.873986Z digest=sha256:103a527b42ee7e80ee2f87b1b1dd5f1ea0730f6178b14c7fb22d99b9551a8583

Observation ea7c3680-82eb-4d01-b48e-372642163c85 · outbound

This paper cites Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-05T14:12:32.011950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:12:32.011950Z digest=sha256:75748a814b9f5e63bbf60a0b5467218c166a572e73bb70a5c911c8b064f65381

Observation 9c862119-69f1-4eef-abdc-db1d2985d5d5 · outbound

This paper cites CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition

Reference 65

Resolution
verified exact
local_arxiv, observed 2026-08-05T14:12:33.337559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:32.092246Z digest=sha256:80ec7cd86664967a867f553f5726af194be6bb1da09535eb3ac8ec895671896d

Observation 10557148-736a-41b6-aa0a-ac9e6c774970 · outbound

This paper cites Exposing deep fakes using inconsistent head poses.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Exposing deep fakes using inconsistent head poses

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:34.765690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:32.225393Z digest=sha256:2bfea530c1ff18ffbd7e11c6411b2adb61629e9a1126c5ce60de4a930a001d41

Observation 51b69acc-9a1d-46d9-ba42-b848babf0d56 · outbound

This paper cites Towards understanding the generalization of deepfake detectors from a game-theoretical view.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Towards understanding the generalization of deepfake detectors from a game-theoretical view

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:34.594145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:32.322754Z digest=sha256:9b74cf381458ce603f336c2cb1b23d1d351ad871bcb36ebb0c2f7df54f89bd04

Observation 68a815cf-a726-4810-a0b6-54b7845a1f83 · outbound

This paper cites Face anti-spoofing via disentangled represen- tation learning.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Face anti-spoofing via disentangled represen- tation learning

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:34.449101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:32.470660Z digest=sha256:b8255de7fa0b210b23ea94a5d6c3bc8c0be052a1ab13c0c3987664b405bdf751

Observation 9bb6214b-8c08-4965-916c-7d35b9f882a9 · outbound

This paper cites X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection

Reference 69

Resolution
verified exact
local_arxiv, observed 2026-08-05T14:12:33.140915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:32.649109Z digest=sha256:c9b76049297d51a1a91048599921ece2669167f39359ed0e64475f017ee83721

Observation d8621a67-31d3-48d8-9a93-a8f2cde6482c · outbound

This paper cites Few-shot learning for misinformation detection based on contrastive models.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Few-shot learning for misinformation detection based on contrastive models

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:34.290802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:32.800390Z digest=sha256:998444f72299dc056f0b6a2c81c9bb68f051ae94cafe66ab3f3bb9144c7202a7

Observation 3e63e9d9-5872-4137-aa3f-e08611ca5edc · outbound

This paper cites Wilddeepfake: A challenging real-world dataset for deepfake detection.

Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization Wilddeepfake: A challenging real-world dataset for deepfake detection

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:12:34.105465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:12:32.909169Z digest=sha256:2c4e8d48f239f02d2ae474697faad6e19276a20cad62984ac00d9587a83e155b

Pith citing papers

No inbound Pith citation observations are available.