A lightweight 3D convolutional neural network for deepfake detection
Open accessAbstract
The rapid development of DeepFake technologies has brought great challenges to the authenticity of video contents. It is of vital importance to develop DeepFake detection methods, among which three-dimensional (3D) convolution neural networks (CNN) have attracted wide interest and achieved satisfying performances. However, there are few 3D CNNs designed for DeepFake detection and the parameters of them are large, which cause heavy memory and storage consumption. In this paper, a lightweight 3D CNN is proposed for DeepFake detection. Channel transformation module is designed to extract features with much fewer parameters in higher level. Serving as spatial-temporal module, 3D CNNs are adopted to fuse the spatial features in time dimension. To suppress frame content and highlight frame texture, spatial rich model features are extracted from the input frames, which helps the spatial-temporal module achieve better performance. Experimental results show that the number of parameters of the proposed network is much less than those of other networks and the proposed network outperforms other state-of-the-art DeepFake detection methods on mainstream DeepFake data sets.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · see citing works
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Carreira, J., & Zisserman, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.502 · Cited by 9,570 · see citing works
3D Convolutional Neural Networks for Human Action Recognition
Ji, S., Xu, W., Yang, M., & Yu, K. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,222 · see citing works
A Closer Look at Spatiotemporal Convolutions for Action Recognition
Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., & Paluri, M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00675 · Cited by 3,672 · see citing works
FaceForensics++: Learning to Detect Manipulated Facial Images
Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Niessner, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00009 · Cited by 3,314 · see citing works
Dlib-ml: A Machine Learning Toolkit
King, D. E. (2009) · Journal of machine learning research · doi:10.5555/1577069.1755843 · Cited by 2,945 · see citing works
MesoNet: a Compact Facial Video Forgery Detection Network
Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. (2018) · 2018 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs.2018.8630761 · Cited by 1,872 · 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
“44 The accuracy of the network with SRM feature is approximately 1% higher than that of the network without SRM features.”
BackgroundInfluentialFace2Face: Real-Time Face Capture and Reenactment of RGB Videos
Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., & Niessner, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.262 · Cited by 1,747 · see citing works
Deepfake Video Detection Using Recurrent Neural Networks
Guera, D., & Delp, E. J. (2018) · 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2018.8639163 · Cited by 1,285 · see citing works
Deepfakes and beyond: A Survey of face manipulation and fake detection
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.014 · Cited by 1,237 · 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
Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos
Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8682602 · Cited by 951 · see citing works
Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations
Matern, F., Riess, C., & Stamminger, M. (2019) · 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw.2019.00020 · Cited by 824 · see citing works
Exposing DeepFake Videos By Detecting Face Warping Artifacts
Li, Y., & Lyu, S. (2018) · arXiv · doi:10.48550/arxiv.1811.00656 · Cited by 785 · see citing works
On the Detection of Digital Face Manipulation
Dang, H., Liu, F., Stehouwer, J., Liu, X., & Jain, A. K. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00582 · Cited by 764 · see citing works
Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition
Hara, K., Kataoka, H., & Satoh, Y. (2017) · 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw.2017.373 · Cited by 726 · see citing works
Multi-task Learning for Detecting and Segmenting Manipulated Facial Images and Videos
Nguyen, H. H., Fang, F., Yamagishi, J., & Echizen, I. (2019) · 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS) · doi:10.1109/btas46853.2019.9185974 · Cited by 549 · see citing works
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2016) · arXiv · doi:10.48550/arxiv.1610.02357 · Cited by 538 · see citing works
DeepFakes: a New Threat to Face Recognition? Assessment and Detection
Korshunov, P., & Marcel, S. (2018) · arXiv · doi:10.48550/arxiv.1812.08685 · Cited by 497 · see citing works
The Deepfake Detection Challenge (DFDC) Preview Dataset
Dolhansky, B., Howes, R., Pflaum, B., Baram, N., & Ferrer, C. C. (2019) · arXiv · doi:10.48550/arxiv.1910.08854 · Cited by 413 · see citing works
Cozzolino, D., Poggi, G., & Verdoliva, L. (2017) · Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security · doi:10.1145/3082031.3083247 · Cited by 349 · see citing works
Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference
Sanderson, C., & Lovell, B. C. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01793-3_21 · Cited by 303 · see citing works
Xiao, Y., Yin, H., Zhang, Y., Qi, H., Zhang, Y., & Liu, Z. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22370 · Cited by 244 · see citing works
ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection
Cozzolino, D., Thies, J., Rössler, A., Riess, C., Nießner, M., & Verdoliva, L. (2018) · arXiv · doi:10.48550/arxiv.1812.02510 · Cited by 221 · see citing works
OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder
Khalid, H., & Woo, S. S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00336 · Cited by 197 · see citing works
Use of a Capsule Network to Detect Fake Images and VideosPreprint
Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12467 · Cited by 163 · see citing works
Deepfake Detection using Spatiotemporal Convolutional NetworksPreprint
de Lima, O., Franklin, S., Basu, S., Karwoski, B., & George, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.14749 · Cited by 159 · see citing works
DeepfakeStack: A Deep Ensemble-based Learning Technique for Deepfake Detection
Rana, M. S., & Sung, A. H. (2020) · 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom) · doi:10.1109/cscloud-edgecom49738.2020.00021 · Cited by 133 · see citing works
A two‐stage privacy protection mechanism based on blockchain in mobile crowdsourcing
Sun, Z., Wang, Y., Cai, Z., Liu, T., Tong, X., & Jiang, N. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22371 · Cited by 110 · see citing works
Spatio-temporal Features for Generalized Detection of Deepfake VideosPreprint
Ganiyusufoglu, I., Ngo, M., Savov, N., Karaoğlu, S., & Gevers, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11844 · Cited by 62 · see citing works
A video is worth more than 1000 lies. Comparing 3DCNN approaches for detecting deepfakes
Wang, Y., & Dantcheva, A. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00089 · Cited by 60 · see citing works
PrivateDL: Privacy‐preserving collaborative deep learning against leakage from gradient sharing
Zhao, Q., Zhao, C., Cui, S., Jing, S., & Chen, Z. (2020) · International Journal of Intelligent Systems · doi:10.1002/int.22241 · Cited by 41 · see citing works
FSSPOTTER: Spotting Face-Swapped Video by Spatial and Temporal Clues
Chen, P., Liu, J., Liang, T., Zhou, G., Gao, H., Dai, J., & Han, J. (2020) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme46284.2020.9102914 · Cited by 36 · see citing works
Self-citationPrivacy preservation for image data: A GAN‐based method
Chen, Z., Zhu, T., Xiong, P., Wang, C., & Ren, W. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22356 · Cited by 33 · see citing works
Fake Video Detection With Certainty-Based Attention Network
Choi, D. H., Lee, H. J., Lee, S., Kim, J. U., & Ro, Y. M. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190655 · Cited by 26 · see citing works
Private rank aggregation under local differential privacy
Yan, Z., Li, G., & Liu, J. (2020) · International Journal of Intelligent Systems · doi:10.1002/int.22261 · Cited by 23 · see citing works
Self-citationPrivacy‐aware PKI model with strong forward security
Li, F., Liu, Z., Li, T., Ju, H., Wang, H., & Zhou, H. (2020) · International Journal of Intelligent Systems · doi:10.1002/int.22283 · Cited by 23 · see citing works
Video frame interpolation via optical flow estimation with image inpainting
Liu, X., Liu, H., & Lin, Y. (2020) · International Journal of Intelligent Systems · doi:10.1002/int.22285 · Cited by 22 · see citing works
Zheng, W., Yan, L., Gou, C., & Wang, F. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22372 · Cited by 15 · see citing works
Padilla, M., Martínez, J. M., & Herranz, L. (2006) · International Journal of Intelligent Systems · doi:10.1002/int.20160 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 6
- Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Gen- erative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Pro- cessing Systems, NIPS'14. Cambridge, MA; 2014:2672-2680.
- Chen Z, Zhu T, Xiong P, Wang C, Ren W. Privacy preservation for image data: a gan-based method. Int J Intell Syst. 2021;36(4):1668-1685.
- Sabir E, Cheng J, Jaiswal A, AbdAlmageed W, Masi I, Natarajan P. Recurrent convolutional strategies for face manipulation detection in videos. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops; 2019:80-87.
- Rössler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nießner M. Faceforensics++: learning to detect manipulated facial images. In: Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea (South); 2019:1-11.
- LI X, YU K. A deepfakes detection technique based on two-stream network. J Cyber Security. 2020; 5(2): 84-91.
- Carreira J, Zisserman A. Quo vadis, action recognition? A new model and the kinetics dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI; 2017:4724-4733.
Cited by
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Deepfake video detection: challenges and opportunities
Kaur, A., Noori Hoshyar, A., Saikrishna, V., Firmin, S., & Xia, F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10810-6 · Cited by 214 · see citing works
“Recent advancements in AI-powered deepfakes have even amplified the issue (Liu et al. 2021b).”
A Novel Blockchain-Based Deepfake Detection Method Using Federated and Deep Learning Models
Heidari, A., Navimipour, N. J., Dag, H., Talebi, S., & Unal, M. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10255-7 · Cited by 164 · see citing works
A Review of Image Processing Techniques for Deepfakes
Shahzad, H. F., Rustam, F., Flores, E. S., Mazón, J. L. V., Díez, I. D. L. T., & Ashraf, I. (2022) · Sensors · doi:10.3390/s22124556 · Cited by 88 · see citing works
Heidari, A., Toumaj, S., Navimipour, N. J., & Unal, M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105461 · Cited by 67 · see citing works
A systematic literature review on the effectiveness of deepfake detection techniques
Stroebel, L., Llewellyn, M., Hartley, T., Ip, T. S., & Ahmed, M. (2023) · Journal of Cyber Security Technology · doi:10.1080/23742917.2023.2192888 · Cited by 66 · see citing works
HolisticDFD: Infusing spatiotemporal transformer embeddings for deepfake detection
Raza, M. A., Malik, K. M., & Haq, I. U. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119352 · Cited by 39 · see citing works
Digital Face Manipulation Creation and Detection: A Systematic Review
Dang, M. H., & Nguyen, T. N. (2023) · Electronics · doi:10.3390/electronics12163407 · Cited by 34 · see citing works
TAN-GFD: generalizing face forgery detection based on texture information and adaptive noise mining
Zhao, Y. X., Jin, X., Gao, S., Wu, L., Yao, S., & Jiang, Q. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04462-2 · Cited by 29 · see citing works
DMFF-Net: Double-stream multilevel feature fusion network for image forgery localization
Xia, X., Su, L., Wang, S. P., & Li, X. Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107200 · Cited by 23 · see citing works
Deepfake Generation and Detection - An Exploratory Study
Garg, D., & Gill, R. (2023) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon59197.2023.10434896 · Cited by 27 · see citing works
DeepFake video detection: Insights into model generalisation — A Systematic review
Ramanaharan, R., Guruge, D. B., & Agbinya, J. I. (2025) · Data and Information Management · doi:10.1016/j.dim.2025.100099 · Cited by 25 · see citing works
Document images forgery localization using a two‐stream network
Xu, W., Luo, J., Zhu, C., Lu, W., Zeng, J., Shi, S., & Lin, C. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22792 · Cited by 21 · see citing works
Self-citationPR‐NET: Progressively‐refined neural network for image manipulation localization
Shi, Z., Chang, C., Chen, H., Du, X., & Zhang, H. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22822 · Cited by 21 · see citing works
Ataş, S., İlhan, İ., & Karakse, M. (2022) · doi:10.1109/it54280.2022.9743542 · Cited by 18 · see citing works
Detecting Deepfake Videos using Spatiotemporal Trident Network
Lin, K., Han, W., Li, S., Gu, Z., Zhao, H., & Mei, Y. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3623639 · Cited by 17 · see citing works
Contrastive Learning based Multi-task Network for Image Manipulation Detection
Yin, Q., Wang, J., Lu, W., & Luo, X. (2022) · Signal Processing · doi:10.1016/j.sigpro.2022.108709 · Cited by 14 · see citing works
Self-citationDeepFake Videos Detection Using Crowd Computing
Salini, Y., & HariKiran, J. (2024) · International Journal of Information Technology · doi:10.1007/s41870-023-01494-2 · Cited by 13 · see citing works
Deepfake source detection in a heart beat
Çiftçi, U. A., Demir, İ., & Yin, L. (2023) · The Visual Computer · doi:10.1007/s00371-023-02981-0 · Cited by 13 · see citing works
DRVAT: Exploring RSSI series representation and attention model for indoor positioning
Ai, H., Sun, X., Tao, J., Liu, M., & Li, S. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22712 · Cited by 13 · see citing works
Analysis of the current state of deepfake techniques-creation and detection methods
Abu-Ein, A. A.-K. H., Al-Hazaimeh, O. M., Dawood, A. M., & Swidan, A. I. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v28.i3.pp1659-1667 · Cited by 13 · see citing works
Spatiotemporal Inconsistency Learning and Interactive Fusion for Deepfake Video Detection
Zhang, D., Zhu, W., Liao, X., Qi, F., Yang, G., & Ding, X. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3664654 · Cited by 13 · see citing works
Liu, B. (2024) · Scientific Reports · doi:10.1038/s41598-024-72343-w · Cited by 13 · see citing works
DeepFake detection against adversarial examples based on D‐VAEGAN
Chen, P., Xu, M., & Qi, J. (2023) · IET Image Processing · doi:10.1049/ipr2.12973 · Cited by 11 · see citing works
Attention‐guided transformation‐invariant attack for black‐box adversarial examples
Zhu, J., Dai, F., Yu, L., Xie, H., Wang, L., Wu, B., & Zhang, Y. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22808 · Cited by 11 · see citing works
A study on deep fake identification techniques using deep learning
Lal, K., & Saini, M. L. (2023) · AIP conference proceedings · doi:10.1063/5.0154828 · Cited by 10 · see citing works
MC‐Net: Learning mutually‐complementary features for image manipulation localization
Xu, D., Shen, X., Lyu, Y., Du, X., & Feng, F. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22826 · Cited by 10 · see citing works
DeepFake Videos Detection via Spatiotemporal Inconsistency Learning and Interactive Fusion
Ding, X., Zhu, W., & Zhang, D. (2022) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/secon55815.2022.9918605 · Cited by 7 · see citing works
Recent Advancements in the Field of Deepfake Detection
Krueger, N., Vanamala, M., & Dave, R. (2023) · International Journal of Computer Science and Information Technology · doi:10.5121/ijcsit.2023.15401 · Cited by 6 · see citing works
Interpolation graph convolutional network for 3D point cloud analysis
Liu, Y., Yao, L., Li, B., Sammut, C., & Chang, X. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.23087 · Cited by 6 · see citing works
Progressive selection‐channel networks for image steganalysis
Wu, T., Wang, L., Zhai, L., Fang, C., & Zhang, M. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22888 · Cited by 6 · see citing works
Anusha, T., & Srinagesh, A. (2025) · International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI) · doi:10.1109/icmsci62561.2025.10894187 · Cited by 4 · see citing works
Adversarial Reality for Evading Deepfake Image Detectors
Çiftçi, U. A., Solar, N., Greene, E., Saremsky, S. R., & Demir, İ. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00171 · Cited by 4 · see citing works
Raj, M. T., Arunachalam, M., Ramalakshmi, R., Ramaraj, K., Arunachalam, M., & Kaleeswari, P. (2023) · International Conference on Automation, Computing and Renewable Systems (ICACRS) · doi:10.1109/icacrs58579.2023.10405046 · Cited by 4 · see citing works
Exposing the Forgery Clues of DeepFakes via Exploring the Inconsistent Expression Cues
Liu, J., Wang, L., Wang, R., Ke, J., Ye, X., & Wu, Y. (2025) · International Journal of Intelligent Systems · doi:10.1155/int/7945646 · Cited by 5 · see citing works
Self-citationTFD-Video: Threshold-Aware Federated Deepfake Detection for Video Forensics
Al-Fehani, M., Albaseer, A. M., & Al-Kuwari, S. (2026) · IEEE Access · doi:10.1109/access.2026.3660914 · Cited by 3 · see citing works
Wang, J., Lei, J., Li, S., & Zhang, J. (2025) · Symmetry · doi:10.3390/sym17071037 · Cited by 3 · see citing works
Jabbar, N., Bhatti, S. M., Rashid, M., Jaffar, A., & Akram, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0326565 · Cited by 5 · see citing works
Inequality distance hyperplane multiclass support vector machines
Lv, W., Li, T., Ren, H., Zeng, S., & Zhou, J. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22764 · Cited by 2 · see citing works
A Multi-Attention Feature Distillation Neural Network for Lightweight Single Image Super-Resolution
Zhang, Y., Lin, X.-Y., Yang, H. W., He, J., Qing, L., He, X., Li, Y. C., & Chen, H. (2024) · International Journal of Intelligent Systems · doi:10.1155/2024/3255233 · Cited by 2 · see citing works
Adversarial Reality for Frame-based Deepfake Detectors
Çiftçi, U. A., Solar, N., Greene, E. M., Rhodes, A. D., & Demir, İ. (2025) · doi:10.1145/3746265.3759662 · Cited by 1 · see citing works
A Survey on Deepfake Detection Techniques for Watermarking and Digital Forensics Using Deep Learning
Suhasini, T. S., Шарма, М., & Mishra, D. (2025) · doi:10.1109/cises66934.2025.11265509 · Cited by 1 · see citing works
Lv, J., Wang, N., Ye, Y., & Li, W. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3050180
An Overview of Face Image Forgery Detection
He, D., Liu, S., Jin, X., Huang, S., & Jiang, Q. (2022) · Current Chinese Computer Science · doi:10.2174/2665997202666220818122419
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Analysis on DeepFake Dataset, Manipulation and Detection Techniques: A Review
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Analysis of Psychomotor Ability Evaluation Method Based on Lightweight Convolutional Neural Networks
Tang, J., & Shi, H. (2023) · International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI) · doi:10.1109/icdacai59742.2023.00099
Detecting Deepfake Videos Dilemma Revisited: Finding Gaps and Shortcomings
Kharbat, F. F., & Badawi, L. (2023) · International Arab Conference on Information Technology (ACIT) · doi:10.1109/acit58888.2023.10453729
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RuleBoost: A Neuro-Symbolic Framework for Robust Deepfake Detection
Raza, M. A., Malik, K. M., & Haq, I. U. (2024) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb62174.2024.10744498
A Novel Consensus Mechanism Using Publicly Verifiable Randomness for Blockchain Networks
Saranya, S. S., Sivaraj, R., & Murugasamy, V. (2025) · Tehnicki vjesnik - Technical Gazette · doi:10.17559/tv-20240902001963
Lightweight sign language intelligent recognition model based on improved R-C3D
Chen, H., & Di, C. (2025) · Egyptian Informatics Journal · doi:10.1016/j.eij.2025.100801
STFANet: A spatial and temporal feature aggregation network for fake face detection in videos
YAO, G. L. J., Yang, G., Liu, X., & Chen, L. (2025) · PLoS ONE · doi:10.1371/journal.pone.0337340
Lightweight Deepfake Detection Using Transfer Learning and Swarm-Based Optimization
Pundir, S., Sharma, A., Pundir, S., & Shrivastava, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-18846-5_22
Moon, A.-S., Lee, J. K., & Lee, J. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21771-z
Deep-fake detection of digital misinformation over social media: a comprehensive review
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A Real-Time Face Swap Detection Model for Video Chatting Scams
Li, X., Weng, Y., Zhang, J., Liu, H., & Wang, H. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-22542-9_20
- Authors
- Liu, J., Zhu, K., Lu, W., Luo, X., & Zhao, X.
- Affiliations
- Sun Yat-sen University (CN)
- State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China
- Chinese Academy of Sciences (CN)
- Corresponding author
- Lu, Wei
- Venue
- International Journal of Intelligent Systems
- Publisher
- Wiley
- Published
- 2021-06-03
- Volume
- 36
- Issue
- 9
- Pages
- 4990-5004
- ISSN
- 0884-8173 (ISSN-L)
- 1098-111X
- OpenAlex
- W3166428200
- Cited by
- 57
- References
- 36
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 7 · 2023: 18 · 2022: 13 · 2021: 3
- FWCI
- 3.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- International Journal of Intelligent Systems (publishedVersion)
- Funders
- National Natural Science Foundation of China (U1736118, U2001202, 62072480)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- deepfake detection, 3D convolutional neural networks, lightweight neural networks, spatiotemporal feature fusion, video authenticity, parameter efficiency
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