Fine-Grained Multimodal DeepFake Classification via Heterogeneous Graphs

Advances in neural information processing systems 7

(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759 · see citing works

The graph neural network model

Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582 · see citing works

Hearing lips and seeing voices

MCGURK, H., & MACDONALD, J. (1976) · Nature · doi:10.1038/264746a0 · Cited by 6,183 · see citing works

Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions

Shen, J., Pang, R., Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., Chen, Z., Zhang, Y., Wang, Y., Skerrv-Ryan, R., et al. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8461368 · Cited by 2,791 · see citing works

Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics

Li, Y., Yang, X., Sun, P., Qi, H., & Lyu, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00327 · Cited by 1,846 · cited 4× · see citing works

Face X-Ray for More General Face Forgery Detection

Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., & Guo, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00505 · Cited by 1,211 · see citing works

MoCoGAN: Decomposing Motion and Content for Video Generation

Tulyakov, S., Liu, M.-Y., Yang, X., & Kautz, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00165 · Cited by 1,076 · see citing works

Thinking in Frequency: Face Forgery Detection by Mining Frequency-Aware Clues

Qian, Y., Yin, G., Sheng, L., Chen, Z., & Shao, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58610-2_6 · Cited by 1,058 · see citing works

A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild

Prajwal, K. R., Mukhopadhyay, R., Namboodiri, V. P., & Jawahar, C. V. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413532 · Cited by 897 · see citing works

MMGCN

Wei, Y., Wang, X., Nie, L., He, X., Hong, R., & Chua, T. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3351034 · Cited by 735 · see citing works

Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain

Liu, H., Li, X., Zhou, W., Chen, Y., He, Y., Xue, H., Zhang, W., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00083 · Cited by 600 · see citing works

DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection

Jiang, L., Li, R., Wu, W., Qian, C., & Loy, C. C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00296 · Cited by 599 · see citing works

Learning Human-Object Interactions by Graph Parsing Neural Networks

Qi, S., Wang, W., Jia, B., Shen, J., & Zhu, S. C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_25 · Cited by 597 · see citing works

Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection

Haliassos, A., Vougioukas, K., Petridis, S., & Pantic, M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00500 · Cited by 537 · see citing works

Transfer learning from speaker verification to multispeaker text-to-speech synthesis

Jia, Y., Zhang, Y., Weiss, R. J., Wang, Q., Shen, J., Ren, F., Chen, Z., Nguyen, P., Pang, R., Moreno, I. L., et al. (2018) · arXiv · doi:10.48550/arxiv.1806.04558 · Cited by 519 · see citing works

Fast Face-Swap Using Convolutional Neural Networks

Korshunova, I., Shi, W., Dambre, J., & Theis, L. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.397 · Cited by 501 · see citing works

WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection

Zi, B., Chang, M., Chen, J., Ma, X., & Jiang, Y.-G. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413769 · Cited by 448 · see citing works

Cross-Sentence N -ary Relation Extraction with Graph LSTMs

Peng, N., Poon, H., Quirk, C., Toutanova, K., & Yih, W.-T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00049 · Cited by 444 · see citing works

End-to-End Reconstruction-Classification Learning for Face Forgery Detection

Cao, J., Ma, C., Yao, T., Chen, S., Ding, S., & Yang, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00408 · Cited by 428 · see citing works

Learning Self-Consistency for Deepfake Detection

Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., & Xia, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01475 · Cited by 383 · see citing works

A New Feature for Automatic Speaker Verification Anti-Spoofing: Constant Q Cepstral Coefficients

Todisco, M., Delgado, H., & Evans, N. (2016) · The Speaker and Language Recognition Workshop (Odyssey) · doi:10.21437/odyssey.2016-41 · Cited by 340 · see citing works

Local Relation Learning for Face Forgery Detection

Chen, S., Yao, T., Chen, Y., Ding, S., Li, J., & Ji, R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16193 · Cited by 334 · see citing works

Exploring Temporal Coherence for More General Video Face Forgery Detection

Zheng, Y., Bao, J., Chen, D., Zeng, M., & Wen, F. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01477 · Cited by 320 · see citing works

Emotions Don't Lie: An Audio-Visual Deepfake Detection Method using Affective Cues

Mittal, T., Bhattacharya, U., Chandra, R., Bera, A., & Manocha, D. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413570 · Cited by 319 · see citing works

DualGNN: Dual Graph Neural Network for Multimedia Recommendation

Wang, Q., Wei, Y., Yin, J., Wu, J., Song, X., & Nie, L. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3138298 · Cited by 293 · see citing works

Automatic Speaker Verification Spoofing and Deepfake Detection Using Wav2vec 2.0 and Data Augmentation

Tak, H., Todisco, M., Wang, X., Jung, J.-W., Yamagishi, J., & Evans, N. (2022) · The Speaker and Language Recognition Workshop (Odyssey 2022) · doi:10.21437/odyssey.2022-16 · Cited by 237 · see citing works

Neural Voice Cloning with a Few SamplesPreprint

Arık, S. Ö., Chen, J., Peng, K., Ping, W., & Zhou, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.06006 · Cited by 229 · see citing works

Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization

Chugh, K., Gupta, P., Dhall, A., & Subramanian, R. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413700 · Cited by 225 · see citing works

Joint Audio-Visual Deepfake Detection

Zhou, Y., & Lim, S.-N. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01453 · Cited by 217 · see citing works

End-to-end spectro-temporal graph attention networks for speaker verification anti-spoofing and speech deepfake detection

Tak, H., Jung, J.-W., Patino, J., Kamble, M., Todisco, M., & Evans, N. (2021) · 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge · doi:10.21437/asvspoof.2021-1 · Cited by 197 · see citing works

Sharp Multiple Instance Learning for DeepFake Video Detection

Li, X., Lang, Y., Chen, Y., Mao, X., He, Y., Wang, S., Xue, H., & Lu, Q. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3414034 · Cited by 192 · see citing works

Countering Malicious DeepFakes: Survey, Battleground, and Horizon

Juefei-Xu, F., Wang, R., Huang, Y., Guo, Q., Ma, L., & Liu, Y. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01606-8 · Cited by 191 · cited 1× · see citing works

Spatiotemporal Inconsistency Learning for DeepFake Video Detection

Gu, Z., Chen, Y., Yao, T., Ding, S., Li, J., Huang, F., & Ma, L. (2021) · Proceedings of the 29th ACM International Conference on Multimedia · doi:10.1145/3474085.3475508 · Cited by 189 · see citing works

You Said That?: Synthesising Talking Faces from Audio

Jamaludin, A., Chung, J. S., & Zisserman, A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01150-y · Cited by 189 · see citing works

Towards End-to-End Synthetic Speech Detection

Hua, G., Teoh, A. B. J., & Zhang, H. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3089437 · Cited by 186 · see citing works

Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection

Haliassos, A., Mira, R., Petridis, S., & Pantic, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01453 · Cited by 180 · see citing works

UIA-ViT: Unsupervised Inconsistency-Aware Method Based on Vision Transformer for Face Forgery Detection

Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., & Yu, N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20065-6_23 · Cited by 152 · see citing works

Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks

Yang, X., Feng, S., Zhang, Y., & Wang, D. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.28 · Cited by 145 · see citing works

Voice-Face Homogeneity Tells Deepfake

Cheng, H. H., Guo, Y., Wang, T., Li, Q., Chang, X., & Nie, L. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3625231 · Cited by 123 · see citing works

A Graph Attention Spatio-temporal Convolutional Network for 3D Human Pose Estimation in Video

Liu, J.-F., Rojas, J., Li, Y., Liang, Z.-J., Guan, Y.-S., Xi, N., & Zhu, H. (2021) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra48506.2021.9561605 · Cited by 100 · see citing works

Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization

Cai, Z., Stefanov, K., Dhall, A., & Hayat, M. (2022) · 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta56598.2022.10034605 · Cited by 99 · see citing works

Dynamic Difference Learning With Spatio–Temporal Correlation for Deepfake Video Detection

Yin, Q., Lu, W., Li, B., & Huang, J. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3290752 · Cited by 92 · see citing works

Detection of Deepfake Videos Using Long-Distance Attention

Lu, W., Liu, L., Zhang, B., Luo, J., Zhao, X., Zhou, Y., & Huang, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3233063 · Cited by 75 · see citing works

Successive Graph Convolutional Network for Image De-raining

Fu, X., Qi, Q., Zha, Z.-J., Ding, X., Wu, F., & Paisley, J. W. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01428-6 · Cited by 56 · see citing works

Few-Shot Audio Classification with Attentional Graph Neural Networks

Zhang, S., Qin, Y., Sun, K., & Lin, Y. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1532 · Cited by 47 · see citing works

AutoEncoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis

Cao, B., Bi, Z., Hu, Q., Zhang, H., Wang, N., Gao, X., & Shen, D. G. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01791-0 · Cited by 45 · see citing works

Multimodal Graph Networks for Compositional Generalization in Visual Question Answering

Saqur, R., & Narasimhan, K. (2020) · neural information processing systems · Cited by 35 · see citing works

Hierarchical Conditional Relation Networks for Multimodal Video Question Answering

Le, T. M., Le, V., Venkatesh, S., & Tran, T. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01514-3 · Cited by 27 · see citing works

Sentimental Visual Captioning using Multimodal Transformer

Wu, X., & Li, T. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01752-7 · Cited by 19 · see citing works

Recurrent Graph Neural Networks for Video Instance Segmentation

Brissman, E., Johnander, J., Danelljan, M., & Felsberg, M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01703-8 · Cited by 18 · see citing works

DeepFake detection in the AIGC era: A survey, benchmarks, and future perspectives

Xie, S., Qiao, T., Li, S., Zhang, X., Zhou, J., & Feng, G. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103740 · Cited by 14 · see citing works

Multi-modal Deepfake Detection via Multi-task Audio-Visual Prompt Learning

Miao, H., Guo, Y., Liu, Z., & Wang, Y. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i1.32042 · Cited by 8 · see citing works

X3A: Efficient Multimodal Deepfake Detection with Score-Level Fusion

Park, C. S., Moon, B., Jeon, M., Jung, J.-W., & Woo, S. S. (2025) · ACM Symposium on Applied Computing · doi:10.1145/3672608.3707934 · Cited by 8 · see citing works

A Contemporary and Comprehensive Bibliometric Exposition on Deepfake Research and Trends

AbdulQudus, A. B., Amodu, O. A., Bukar, U. A., Mahmood, R. A. R., Zakaria, A. F., Queen, S.-O., & Hanapi, Z. M. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.061427 · Cited by 6 · see citing works

Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges

Liu, P., Tao, Q., & Zhou, J. T. (2024) · arXiv · doi:10.48550/arxiv.2406.06965 · Cited by 6 · see citing works

Self-supervised Bidirectional Synchronization Estimation for Multimodal Deepfake Detection with Short-term Dependency

Xiao, M., Ye, J., Liu, B., Gao, Z., Xu, K., & Wang, X. (2025) · International Conference on Multimedia Retrieval · doi:10.1145/3731715.3733429 · Cited by 4 · see citing works

FauForensics: Boosting Audio-Visual Deepfake Detection With Facial Action Units

Wang, J., Wu, B., Liu, L., & Liu, Q. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3674401 · Cited by 7 · see citing works

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation

Guo, M., Yin, Q., Lu, W., & Luo, X. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755495 · Cited by 3 · see citing works

Analytic Synaptic Dynamic Scaling Balancer for Multimodal Deepfake Continual Detection

Xiao, M., Ye, J., Liu, B., Gao, Z., Xu, K., & Wang, X. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755680 · Cited by 3 · see citing works

A Review of Tools and Technologies to Combat Deepfakes

Erokhin, D., & Komendantova, N. (2026) · Information · doi:10.3390/info17040347 · Cited by 2 · see citing works

AVSCNet: A dual-branch network for synchronization detection and content consistency learning in audio-video forgery detection

Zhu, G., Li, H.-S., Xia, H., & Song, S. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133067 · Cited by 2 · see citing works

Deepfakes: A comprehensive survey on techniques, challenges and future directions

Usmani, S., Kumar, D. S., & Sadhya, D. (2026) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2026.111210 · Cited by 2 · see citing works

A Multimodal Deviation Perceiving Framework for Weakly-Supervised Temporal Forgery Localization

Xu, W., Wu, J., Lu, W., Luo, X., & Wang, Q. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755534 · Cited by 2 · see citing works

BFC-Net: Boundary-Frame cross graph attention network for partially spoofed audio localization

Zhou, Y., Xue, Z., Senhadji, L., Shu, H., & Wu, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130867 · Cited by 1 · see citing works

Context-Aware TFL: A Universal Context-Aware Contrastive Learning Framework for Temporal Forgery Localization

Yin, Q., Lu, W. D., Luo, X., Yang, R., & Cao, X. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3680621

Audio-Visual Deepfake Generation and Detection: An Exploratory Survey

Xu, H., An, Y., Fu, P., Fan, Z., Li, B., Liu, J., Liang, Y., & Yu, M. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463954

Are DeepFakes Realistic Enough? Exploring Semantic Mismatch as a Novel Challenge

Deshmukh, S. N., Hambarde, K., Costa, J. C., Proença, H. P. M. C., & Roxo, T. (2026) · IEEE Access · doi:10.1109/access.2026.3730281

Multimodal Deepfake Detection with Quantum State Inspired Analytic Incremental Adaptability Learning

Ye, J., Xiao, M., Liu, B., Hu, H., Gao, Z., Fu, S., Xu, K., & Wang, H. (2026) · International Conference on Multimedia Retrieval · doi:10.1145/3805622.3810739

Adaptive Affinity Memorization With Layer Mutation for Multimodal Deepfake Continual Detection

Xiao, M., Ye, J., Liu, B., Gao, Z., Chen, W., Li, T., Wang, H., & Xu, K. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3707760

CVDSC-Net: A synchronization-content decoupling dual-branch network for audio-video forgery detection

Zhu, G., & Li, H.-S. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134853

Leave No Stone Unturned: Uncovering Holistic Audio-Visual Intrinsic Coherence for Deepfake Detection

Peng, J., Wang, Y., Li, Y., Kong, L., & Hong, X. (2026) · doi:10.1109/cvprf71909.2026.00639

ForgeFinder: Perceptive Multimodal Deepfake Detection via Multi-grained Forgery Localization

Liu, B., Liu, B., Ding, M., & Zhu, T. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3778030 · Cited by 1 · see citing works

Attention-based Multimodal learning framework for Generalized Audio-Visual Deepfake Detection

Masood, M., Javed, A., & Irtaza, A. (2023) · Preprint (Research Square) · doi:10.21203/rs.3.rs-3415144/v1 · Cited by 6 · see citing works

Constructing Audio-Visual Heterogeneous Graphs for Deepfake Detection

Xie, N., Li, J., Deng, Y., Guo, M., & Yang, L. (2025) · Signals and communication technology · doi:10.1007/978-3-031-94973-9_4

Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and Localization

Katamneni, V. S., & Rattani, A. (2024) · 2024 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb62174.2024.10744480 · Cited by 20 · see citing works

MIS-AVoiDD: Modality Invariant and Specific Representation for Audio-Visual Deepfake Detection

Katamneni, V. S., & Rattani, A. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00207 · Cited by 28 · see citing works