CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation | Paper Citation Record

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This paper cites Mesonet: a compact facial video forgery detection network.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Mesonet: a compact facial video forgery detection network

Reference 1

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This paper cites A review of modern audio deepfake detection methods: challenges and future directions.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation A review of modern audio deepfake detection methods: challenges and future directions

Reference 2

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This paper cites Do you really mean that? content driven audio-visual deepfake dataset and multimodal method for temporal forgery localization.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Do you really mean that? content driven audio-visual deepfake dataset and multimodal method for temporal forgery localization

Reference 3

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This paper cites Quo vadis, action recognition? a new model and the kinetics dataset.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Quo vadis, action recognition? a new model and the kinetics dataset

Reference 4

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CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Unresolved cited work

Reference 5

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

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection

Reference 6

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This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation A Simple Framework for Contrastive Learning of Visual Representations

Reference 7

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This paper cites Exploring simple siamese representation learning.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Exploring simple siamese representation learning

Reference 8

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This paper cites Sophia Koepke, Ying Shan, and Zeynep Akata.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Sophia Koepke, Ying Shan, and Zeynep Akata

Reference 9

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This paper cites V oice-face homogeneity tells deepfake.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation V oice-face homogeneity tells deepfake

Reference 10

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This paper cites Can We Leave Deepfake Data Behind in Training Deepfake Detector?.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Can We Leave Deepfake Data Behind in Training Deepfake Detector?

Reference 11

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

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Xception: Deep learning with depthwise separable convolutions

Reference 12

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

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Not made for each other- audio-visual dissonance-based deepfake detection and localization

Reference 13

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This paper cites Audio-visual person-of-interest deepfake detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Audio-visual person-of-interest deepfake detection

Reference 14

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This paper cites Dong, Jin Wang, Renhe Ji, Jiajun Liang, Haoqiang Fan, and Zheng Ge.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Dong, Jin Wang, Renhe Ji, Jiajun Liang, Haoqiang Fan, and Zheng Ge

Reference 15

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This paper cites www.github.com/MarekKowalski/FaceSwap Accessed 2021-04-24.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation www.github.com/MarekKowalski/FaceSwap Accessed 2021-04-24

Reference 16

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This paper cites Self-supervised video forensics by audio-visual anomaly detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Self-supervised video forensics by audio-visual anomaly detection

Reference 17

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This paper cites Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network

Reference 18

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This paper cites Imagebind one embedding space to bind them all.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Imagebind one embedding space to bind them all

Reference 19

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This paper cites Leveraging real talking faces via self-supervision for robust forgery detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Leveraging real talking faces via self-supervision for robust forgery detection

Reference 20

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This paper cites Lips don’t lie: A generalisable and robust approach to face forgery detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Lips don’t lie: A generalisable and robust approach to face forgery detection

Reference 21

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This paper cites Masked autoencoders are scalable vision learners.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Masked autoencoders are scalable vision learners

Reference 22

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This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 23

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This paper cites Lora: Low-rank adaptation of large language models.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Lora: Low-rank adaptation of large language models

Reference 24

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This paper cites Avfakenet: A unified end-to-end dense swin transformer deep learning model for audio-visual deepfakes detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Avfakenet: A unified end-to-end dense swin transformer deep learning model for audio-visual deepfakes detection

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This paper cites Information theory and statistical mechanics.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Information theory and statistical mechanics

Reference 26

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CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Unresolved cited work

Reference 27

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This paper cites FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset

Reference 28

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This paper cites Fast face-swap using convolu- tional neural networks.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Fast face-swap using convolu- tional neural networks

Reference 29

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This paper cites Face x-ray for more general face forgery detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Face x-ray for more general face forgery detection

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This paper cites A Survey on Speech Deepfake Detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation A Survey on Speech Deepfake Detection

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This paper cites Celeb-df: A large-scale challenging dataset for deepfake forensics.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Celeb-df: A large-scale challenging dataset for deepfake forensics

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This paper cites Lips are lying: Spotting the temporal inconsistency between audio and visual in lip-syncing deepfakes.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Lips are lying: Spotting the temporal inconsistency between audio and visual in lip-syncing deepfakes

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This paper cites Exploiting visual artifacts to expose deepfakes and face manipulations.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Exploiting visual artifacts to expose deepfakes and face manipulations

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

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Emo- tions don’t lie: An audio-visual deepfake detection method using affective cues

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This paper cites Does audio deepfake detection generalize? arXiv preprint arXiv:2203.16263, 2022.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Does audio deepfake detection generalize? arXiv preprint arXiv:2203.16263, 2022

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This paper cites Nguyen, Junichi Yamagishi, and Isao Echizen.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Nguyen, Junichi Yamagishi, and Isao Echizen

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This paper cites Towards universal fake image detectors that generalize across generative models.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Towards universal fake image detectors that generalize across generative models

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This paper cites Avff: Audio-visual feature fusion for video deepfake detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Avff: Audio-visual feature fusion for video deepfake detection

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This paper cites Gpt-4: A large multimodal model.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Gpt-4: A large multimodal model

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This paper cites Deepfake generation and detection: A benchmark and survey.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Deepfake generation and detection: A benchmark and survey

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This paper cites Namboodiri, and C.V.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Namboodiri, and C.V

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This paper cites Audio-visual deep neural network for robust person verification.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Audio-visual deep neural network for robust person verification

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This paper cites Learning transferable visual models from natural language supervision.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Learning transferable visual models from natural language supervision

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This paper cites Robust speech recognition via large-scale weak supervision.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Robust speech recognition via large-scale weak supervision

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This paper cites Faceforensics++: Learning to detect manipulated facial images.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Faceforensics++: Learning to detect manipulated facial images

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.606717Z

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This paper cites A comprehensive overview of deepfake: Generation, detection, datasets, and opportunities.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation A comprehensive overview of deepfake: Generation, detection, datasets, and opportunities

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This paper cites Detecting deepfakes with self-blended images.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Detecting deepfakes with self-blended images

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This paper cites Representative forgery mining for fake face detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Representative forgery mining for fake face detection

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.548967Z

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Observation 97f193b6-0890-403e-8b58-e6db7fecf709 · outbound

This paper cites Exploring Depth Information for Detecting Manipulated Face Videos.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Exploring Depth Information for Detecting Manipulated Face Videos

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Resolution: verified exact; local_arxiv, observed 2026-08-07T15:27:01.335494Z

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Observation ae051bda-29c8-4dc1-a08a-1e6535fcd5a8 · outbound

This paper cites Tan, and Haizhou Li.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Tan, and Haizhou Li

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.522275Z

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 0ad396e1-9008-44d0-909e-bce6a7cfc442 · outbound

This paper cites Deep spatial gradient and temporal depth learning for face anti-spoofing.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Deep spatial gradient and temporal depth learning for face anti-spoofing

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.497856Z

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 8654a873-3fb6-43db-b586-f4a3d6f549a9 · outbound

This paper cites Altfreezing for more general video face forgery detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Altfreezing for more general video face forgery detection

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.470186Z

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 febc692d-c351-4c3a-85a6-300c68ce4ca3 · outbound

This paper cites Deepfake Video Detection Using Convolutional Vision Transformer.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Deepfake Video Detection Using Convolutional Vision Transformer

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:58.916792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddcbe21a-8038-433e-9d37-8d3b1f313f5f · outbound

This paper cites Binaural audio-visual localization.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Binaural audio-visual localization

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.445286Z

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 b4697dcc-b1a5-47a8-ad45-b2d8fffe439d · outbound

This paper cites Identity- driven multimedia forgery detection via reference assistance.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Identity- driven multimedia forgery detection via reference assistance

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.421904Z

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 15ce0a97-bc90-4e7e-ac37-97abf628d8bb · outbound

This paper cites Tall: Thumbnail layout for deepfake video detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Tall: Thumbnail layout for deepfake video detection

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.390976Z

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 c6811e39-49e0-4c8c-af3b-f003898121cd · outbound

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

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Transcending forgery specificity with latent space augmentation for generalizable deepfake detection

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.362476Z

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 971ad1d4-6480-44f9-a694-e2f7f915d7e2 · outbound

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

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation DF40: Toward Next-Generation Deepfake Detection

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:59.305225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d877f55-ac55-496c-9d20-c55c0911f808 · outbound

This paper cites GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:59.364495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d25b769b-48ca-4156-bb5f-d47bcb261003 · outbound

This paper cites Ucf: Uncovering common features for generalizable deepfake detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Ucf: Uncovering common features for generalizable deepfake detection

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.337860Z

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 9c056045-81dc-401a-a5b1-e90a2e2f039f · outbound

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

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:59.498474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad1cfbbd-6071-4c67-919d-5c751b1c3a31 · outbound

This paper cites Avoid-df: Audio-visual joint learning for detecting deepfake.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Avoid-df: Audio-visual joint learning for detecting deepfake

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.032256Z

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 be58bad2-38dc-4944-941b-4c71e3c5c80e · outbound

This paper cites Exposing deep fakes using inconsistent head poses.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Exposing deep fakes using inconsistent head poses

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:02.912242Z

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 4939e96a-4e65-4f9e-b493-fec38e132990 · outbound

This paper cites Audio Deepfake Detection: A Survey.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Audio Deepfake Detection: A Survey

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:59.790961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f57e4aa7-b32c-4034-9cac-eafa94f7c76a · outbound

This paper cites Learning natural consistency representation for face forgery video detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Learning natural consistency representation for face forgery video detection

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:02.835435Z

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 0a1ded16-7871-485e-a9f1-31875ef21b89 · outbound

This paper cites Inclusion 2024 Global Multimedia Deepfake Detection Challenge: Towards Multi-dimensional Face Forgery Detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Inclusion 2024 Global Multimedia Deepfake Detection Challenge: Towards Multi-dimensional Face Forgery Detection

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T15:27:00.152871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b594adb-5bb3-4472-b87f-5e8c0bae3f49 · outbound

This paper cites Multi-attentional deepfake detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Multi-attentional deepfake detection

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:02.717608Z

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 e5f35ecf-5e29-4f4a-aa9d-05a896ab8679 · outbound

This paper cites Attention-based spatial-temporal multi-scale network for face anti-spoofing.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Attention-based spatial-temporal multi-scale network for face anti-spoofing

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:02.577494Z

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 98e6c56d-f524-4cca-8c56-5edb2bfa5436 · outbound

This paper cites Exploring temporal coherence for more general video face forgery detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Exploring temporal coherence for more general video face forgery detection

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:02.445239Z

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 ddf7a907-6417-4d79-bc02-88e3e060afbe · outbound

This paper cites Learning deep features for discriminative localization.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Learning deep features for discriminative localization

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:02.349469Z

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 80b7e914-261f-4337-9107-8595853eb3e8 · outbound

This paper cites Makelttalk: speaker-aware talking-head animation.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Makelttalk: speaker-aware talking-head animation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:02.294628Z

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 80b73bbd-895d-4d40-88b8-38d16dff6ea2 · outbound

This paper cites Joint audio-visual deepfake detection.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Joint audio-visual deepfake detection

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:02.134020Z

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 ec5cef83-b4fd-464f-a38d-b37ac7d8645e · outbound

This paper cites Lan- guagebind: Extending video-language pretraining to n-modality by language-based semantic alignment.

CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation Lan- guagebind: Extending video-language pretraining to n-modality by language-based semantic alignment

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:02.020529Z

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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Reference resolution

Outbound references

Pith citing papers