The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Deepfakes Detection with Automatic Face Weighting
Open accessAbstract
Altered and manipulated multimedia is increasingly present and widely distributed via social media platforms. Advanced video manipulation tools enable the generation of highly realistic-looking altered multimedia. While many methods have been presented to detect manipulations, most of them fail when evaluated with data outside of the datasets used in research environments. In order to address this problem, the Deepfake Detection Challenge (DFDC) provides a large dataset of videos containing realistic manipulations and an evaluation system that ensures that methods work quickly and accurately, even when faced with challenging data. In this paper, we introduce a method based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) that extracts visual and temporal features from faces present in videos to accurately detect manipulations. The method is evaluated with the DFDC dataset, providing competitive results compared to other techniques.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The recent availability of large deepfake video databases allowed researchers to train and test detection approaches based on very deep neural networks, such as Xception [1], capsules networks [8], EfficientNet [2], and discriminators of GANs used to generate synthetic images [9], which were shown to outperform the methods based on shallow CNNs, facial physical characteristics [10], [11], [12], [1”
From Improving Generalization of Deepfake Detection With Data Farming and Few-Shot Learning (2022) · cites this paper as [2]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
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
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00482 · Cited by 7,781
Self-citationJoint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2603342 · Cited by 6,214
Research works printed but not linked: 4
- bailey, 2016, Adobe Photoshop A Beginners Guide to Photoshop Lightroom - The 52 Photoshop Lightroom Tricks You Didn’t Know Existed!, 1
- barni, 2019, Copy move sourcetarget disambiguation through multi-branch cnns, arXiv preprint arXiv 1912 12640
- goodfellow, 2016, Deep Learning
- goodfellow, 2014, Generative adversarial nets, Proceedings of Advances in Neural Information Processing Systems, 2672
Cited by
Sorted by citations
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
Deepfake Detection: A Systematic Literature Review
Rana, M. S., Nobi, M. N., Murali, B., & Sung, A. H. (2022) · IEEE Access · doi:10.1109/access.2022.3154404 · Cited by 573
Masood, M., Nawaz, M., Malik, K. M., Javed, A., Irtaza, A., & Malik, H. (2023) · Applied Intelligence · doi:10.1007/s10489-022-03766-z · Cited by 543
Combining EfficientNet and Vision Transformers for Video Deepfake Detection
Coccomini, D. A., Messina, N., Gennaro, C., & Falchi, F. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-06433-3_19 · Cited by 296
A Survey on Deepfake Video Detection
Yu, P., Xia, Z., Fei, J., & Lu, Y. (2021) · IET Biometrics · doi:10.1049/bme2.12031 · Cited by 253
A review on social spam detection: Challenges, open issues, and future directions
Rao, S., Verma, A. K., & Bhatia, T. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115742 · Cited by 237
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
“…al., 2020d), few-shot learning (Mansourifar and Shi, 2020), enhanced MesoNet (Kawa and Syga, 2020; Afchar et al., 2018), a combination with CNN and RNN (Güera and Delp, 2018; Mas Montserrat et al., 2020), DNN with contrastive loss (Hsu et al., 2018), multi-attentional network (Zhao et al., 2021).”
BackgroundDeepfake generation and detection, a survey
Zhang, T. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11733-y · Cited by 176
DeepFake video detection using convolutional vision transformer
Wodajo, D., & Atnafu, S. (2021) · arXiv · doi:10.48550/arxiv.2102.11126 · Cited by 147
FrePGAN: Robust Deepfake Detection Using Frequency-Level Perturbations
Jeong, Y., Kim, D., Ro, Y., & Choi, J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19990 · Cited by 137
A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost
Ismail, A. A., Elpeltagy, M. S., Zaki, M. S., & ElDahshan, K. A. (2021) · Sensors · doi:10.3390/s21165413 · Cited by 146
A Survey on the Detection and Impacts of Deepfakes in Visual, Audio, and Textual Formats
Mubarak, R., Alsboui, T., Alshaikh, O., Inuwa-Dutse, I., Khan, S., & Parkinson, S. (2023) · IEEE Access · doi:10.1109/access.2023.3344653 · Cited by 132
DeepFake detection algorithm based on improved vision transformer
Heo, Y.-J., Yeo, W.-H., & Kim, B. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03867-9 · Cited by 131
A review of deep learning‐based approaches for deepfake content detection
(2024) · doi:10.1111/exsy.13570 · Cited by 116
BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection
Jeong, Y., Kim, D., Min, S., Joe, S., Gwon, Y., & Choi, J. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00293 · Cited by 106
Deep Fake Video Detection Using Transfer Learning Approach
Suratkar, S. S., & Kazi, F. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-022-07321-3 · Cited by 112
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
“3DCNN[33], GRU[34] and ViT [20], [19], [18]).”
DFFMD: A Deepfake Face Mask Dataset for Infectious Disease Era With Deepfake Detection Algorithms
Alnaim, N. M., Almutairi, Z. M., Alsuwat, M. S., Alalawi, H. H., Alshobaili, A., & Alenezi, F. S. (2023) · IEEE Access · doi:10.1109/access.2023.3246661 · Cited by 91
The face deepfake detection challenge
Guarnera, L., Giudice, O., Guarnera, F., Ortis, A., Puglisi, G., Paratore, A., Bui, L. M. Q., Fontani, M., Coccomini, D. A., Caldelli, R., et al. (2022) · Journal of Imaging · doi:10.3390/jimaging8100263 · Cited by 79
Shaaban, O. A., Yıldırım, R., & Alguttar, A. A. (2023) · IEEE Access · doi:10.1109/access.2023.3333866 · Cited by 87
Usman, M. T., Khan, H., Singh, S. K., Lee, M. Y., & Koo, J. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3476477 · Cited by 71
A literature review and perspectives in deepfakes: generation, detection, and applications
Dagar, D., & Vishwakarma, D. K. (2022) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-022-00241-w · Cited by 83
DeepFake on Face and Expression Swap: A Review
Waseem, S., Abu Bakar, S. A. R. S., Ahmed, B. A., Omar, Z., Eisa, T. A. E., & Dalam, M. E. E. (2023) · IEEE Access · doi:10.1109/access.2023.3324403 · Cited by 65
Cai, Z., Ghosh, S., Dhall, A., Gedeon, T., Stefanov, K., & Hayat, M. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103818 · Cited by 63
An Investigation of the Effectiveness of Deepfake Models and Tools
Mukta, M. S. H., Ahmad, J., Raiaan, M. A. K., Islam, S., Azam, S., Ali, M. E., & Jonkman, M. (2023) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan12040061 · Cited by 73
A novel approach for detecting deep fake videos using graph neural network
El-Gayar, M. M., Abouhawwash, M., Askar, S., & Sweidan, S. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00884-y · Cited by 69
Deepfake Video Detection Based on MesoNet with Preprocessing Module
Xia, Z., Qiao, T., Xu, M., Wu, X., Han, L., & Chen, Y. (2022) · Symmetry · doi:10.3390/sym14050939 · Cited by 56
Al-Dhabi, Y., & Zhang, S. (2021) · IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) · doi:10.1109/csaiee54046.2021.9543264 · Cited by 55
Convolutional Neural Network Based on Diverse Gabor Filters for Deepfake Recognition
Khalifa, A. H., Zaher, N. A., Abdallah, A., & Fakhr, M. W. (2022) · IEEE Access · doi:10.1109/access.2022.3152029 · Cited by 61
Assessment framework for deepfake detection in real-world situations
Lu, Y., & Ebrahimi, T. (2024) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-024-00621-8 · Cited by 60
“In this paper, three different types of deepfake detectors [4, 11, 21] are adopted for experiments.”
MethodRecent advances in artificial intelligence for video production system
Huang, Y., Lv, S., Tseng, K.-K., Tseng, P.-J., Xie, X., & Lin, R. F.-Y. (2023) · Enterprise Information Systems · doi:10.1080/17517575.2023.2246188 · Cited by 67
Analyzing Fairness in Deepfake Detection With Massively Annotated Databases
Xu, Y., Terhörst, P., Pedersen, M., & Raja, K. B. (2024) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2024.3365421 · Cited by 70
Forensic Symmetry for DeepFakes
Li, G., Zhao, X., & Cao, Y. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3235579 · Cited by 52
Improving Generalization of Deepfake Detection With Data Farming and Few-Shot Learning
Korshunov, P., & Marcel, S. (2022) · IEEE Transactions on Biometrics, Behavior, and Identity Science · doi:10.1109/tbiom.2022.3143404 · Cited by 50
FFR_FD: Effective and fast detection of DeepFakes via feature point defects
Wang, G., Jiang, Q., Jin, X., & Cui, X. (2022) · Information Sciences · doi:10.1016/j.ins.2022.03.026 · Cited by 50
An Enhanced Deep Learning-Based DeepFake Video Detection and Classification System
Awotunde, J. B., Jimoh, R. G., Imoize, A. L., Abdulrazaq, A. T., Li, C., & Lee, C. (2022) · Electronics · doi:10.3390/electronics12010087 · Cited by 55
Real-Time Advanced Computational Intelligence for Deep Fake Video Detection
Bansal, N., Aljrees, T., Yadav, D. P., Singh, K. U., Kumar, A., Verma, G. K., & Singh, T. (2023) · Applied Sciences · doi:10.3390/app13053095 · Cited by 53
Audio–Visual Synchronization and Lip Movement Analysis for Real-Time Deepfake Detection
Javed, M., Zhang, Z., Dahri, F. H., Laghari, A. A., Krajčík, M., & Almadhor, A. S. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-00911-7 · Cited by 50
Deepfake Detection Scheme Based on Vision Transformer and DistillationPreprint
Heo, Y. J., Choi, Y. J., Lee, Y., & Kim, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.01353 · Cited by 88
Artificial Intelligence and Political Deepfakes: Shaping Citizen Perceptions Through Misinformation
Momeni, M. (2024) · Journal of Creative Communications · doi:10.1177/09732586241277335 · Cited by 69
Deepfake video detection: YOLO-Face convolution recurrent approach
Ismail, A. A., Elpeltagy, M. S., Zaki, M. S., & ElDahshan, K. A. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.730 · Cited by 49
Leveraging edges and optical flow on faces for deepfake detection
Chintha, A., Rao, A. G., Sohrawardi, S. J., Bhatt, K., Wright, M., & Ptucha, R. (2020) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb48548.2020.9304936 · Cited by 43
Liz-López, H., Keïta, M., Ahmed, A. T., Hadid, A., Huertas‐Tato, J., & Camacho, D. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102103 · Cited by 46
Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects
Ur Rehman Ahmed, N., Badshah, A., Adeel, H., Tajammul, A., Daud, A., & Alsahfi, T. (2025) · IEEE Access · doi:10.1109/access.2024.3523288 · Cited by 45
An integrated spatiotemporal-based methodology for deepfake detection
Ismail, A., Elpeltagy, M. S., Zaki, M. S., & ElDahshan, K. A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07633-3 · Cited by 47
John, J. R., & Sherif, B. V. (2022) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac55078.2022.9987265 · Cited by 46
Local attention and long-distance interaction of rPPG for deepfake detection
Wu, J., Zhu, Y. M., Jiang, X., Liu, Y., & Lin, J. (2023) · The Visual Computer · doi:10.1007/s00371-023-02833-x · Cited by 43
An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
Tipper, S., Atlam, H. F., & Lallie, H. S. (2024) · Applied Sciences · doi:10.3390/app14219754 · Cited by 44
Deepfakes: evolution and trends
Gil, R., Virgili‐Gomà, J., López, J. M., & Garcı́a, R. (2023) · Soft Computing · doi:10.1007/s00500-023-08605-y · Cited by 46
Yadav, A. K., & Vishwakarma, D. K. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120898 · Cited by 35
Adaptive Face Forgery Detection in Cross Domain
Song, L., Fang, Z., Li, X., Dong, X., Jin, Z., Chen, Y., & Lyu, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19830-4_27 · Cited by 35
Emergence of deepfakes and video tampering detection approaches: A survey
Kingra, S., Aggarwal, N., & Kaur, N. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13100-x · Cited by 33
Subjective and Objective Evaluation of Deepfake Videos
Korshunov, P., & Marcel, S. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414258 · Cited by 37
A deep learning model for FaceSwap and face-reenactment deepfakes detection
Nawaz, M., Javed, A., & Irtaza, A. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111854 · Cited by 37
Utility of Deep Learning Features for Facial Attributes Manipulation Detection
Akhtar, Z., Mouree, M. R., & Dasgupta, D. (2020) · doi:10.1109/hccai49649.2020.00015 · Cited by 30
Trans-DF: A Transfer Learning-based end-to-end Deepfake Detector
Patel, M. M., Gupta, A., Tanwar, S., & Obaidat, M. S. (2020) · International Conference on Computing, Communication and Automation · doi:10.1109/iccca49541.2020.9250803 · Cited by 36
Deep-fake video detection approaches using convolutional – recurrent neural networks
Suratkar, S. S., Bhiungade, S., Pitale, J., Soni, K., Badgujar, T., & Kazi, F. (2022) · Journal of Control and Decision · doi:10.1080/23307706.2022.2033644 · Cited by 32
Deepfakes Examiner: An End-to-End Deep Learning Model for Deepfakes Videos Detection
Ilyas, H., Irtaza, A., Javed, A., & Malik, K. M. (2022) · International Conference on Open Source Systems and Technologies (ICOSST) · doi:10.1109/icosst57195.2022.10016871 · Cited by 33
A Review of Deep Learning-based Approaches for Deepfake Content Detection
Passos, L. A., Jodas, D., Costa, K. A. P., Júnior, L. A. S., Rodrigues, D., Ser, J. D., Camacho*, D., & Papa, J. (2023) · doi:10.22541/au.169735672.27713914/v1 · Cited by 116
Face manipulated deepfake generation and recognition approaches: a survey
Rehaan, M., Kaur, N., & Kingra, S. (2023) · Smart Science · doi:10.1080/23080477.2023.2268380 · Cited by 34
AW-MSA: Adaptively weighted multi-scale attentional features for DeepFake detection
Yadav, A. K., & Vishwakarma, D. K. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107443 · Cited by 30
Golden ratio based deep fake video detection system with fusion of capsule networks
Dinçer, S., Ulutaş, G., Üstübioğlu, B., Tahaoğlu, G., & Sklavos, N. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109234 · Cited by 29
Improving Generalization of Deepfake Detection by Training for Attribution
Jain, A., Korshunov, P., & Marcel, S. (2021) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp53017.2021.9733468 · Cited by 29
An Overview of Recent Work in Multimedia Forensics
Bhagtani, K., Yadav, A. K. S., Bartusiak, E. R., Xiang, Z., Shao, R., Baireddy, S., & Delp, E. J. (2022) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr54900.2022.00064 · Cited by 25
Self-citationSiamNet: Exploiting source camera noise discrepancies using Siamese Network for Deepfake Detection
Kingra, S., Aggarwal, N., & Kaur, N. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119341 · Cited by 27
Using Deep Learning to Detecting Deepfakes
Mallet, J., Dave, R., Seliya, N., & Vanamala, M. (2022) · 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) · doi:10.1109/iscmi56532.2022.10068449 · Cited by 26
Video authentication detection using deep learning: a systematic literature review
Alrawahneh, A. A.-M., Abdullah, S. N. H. S., Abdullah, S. N. H. S., Kamarudin, N. H., & Taylor, S. K. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05997-8 · Cited by 24
Datasets, clues and state-of-the-arts for multimedia forensics: An extensive review
Yadav, A. K., & Vishwakarma, D. K. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123756 · Cited by 25
Towards Benchmarking and Evaluating Deepfake Detection
Deng, J., Lin, C., Hu, P., Shen, C. Q., Wang, Q., Li, Q. L., & Li, Q. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3369711 · Cited by 38
Jayashre, K., & Amsaprabhaa, M. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103860 · Cited by 26
Face Forgery Detection via Symmetric Transformer
Song, L., Li, X., Fang, Z., Jin, Z., Chen, Y., & Xu, C. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547806 · Cited by 21
A Survey on Deepfake Video Detection Techniques Using Deep Learning
Das, A., Viji, K. S. A., & Sebastian, L. (2022) · International Conference on Next Generation Intelligent Systems (ICNGIS) · doi:10.1109/icngis54955.2022.10079802 · Cited by 23
Deepfake Detection with Clustering-based Embedding Regularization
Zhu, K., Wu, B., & Wang, B. (2020) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc50466.2020.00046 · Cited by 23
MagNet: Detecting Digital Presentation Attacks on Face Recognition
Agarwal, A., Singh, R., Mayank Vatsa, A., & Afzel Noore, B. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.643424 · Cited by 20
Generative Autoregressive Ensembles for Satellite Imagery Manipulation Detection
Montserrat, D. M., Horváth, J., Yarlagadda, S. K., Zhu, F. M., & Delp, E. J. (2020) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs49906.2020.9360909 · Cited by 19
Self-citationQuick Classification of Xception And Resnet-50 Models on Deepfake Video Using Local Binary Pattern
Arini, A., Bahaweres, R. B., & Haq, J. A. (2022) · International Seminar on Machine Learning, Optimization, and Data Science (ISMODE) · doi:10.1109/ismode53584.2022.9742852 · Cited by 20
Robust Frame-Level Detection for Deepfake Videos With Lightweight Bayesian Inference Weighting
Zhou, L., Ma, C., Wang, Z., Zhang, Y., Shi, X., & Wu, L. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3337128 · Cited by 21
Deepfake Detection Using Multiple Data Modalities
Hao, H., Bartusiak, E. R., Güera, D., Montserrat, D. M., Baireddy, S., Xiang, Z., Yarlagadda, S. K., Shao, R., Horváth, J., Yang, J., et al. (2022) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-87664-7_11 · Cited by 20
Self-citationDeepfake Detection Using EfficientNet and XceptionNet
Yasser, B., Hani, J., Elgayar, S., Amgad, O., Ahmed, N., Ebied, H. M., Amr, H., & Salah, M. (2023) · International Conference on Intelligent Computing and Information Systems (ICICIS) · doi:10.1109/icicis58388.2023.10391114 · Cited by 19
Saif, S., Tehsin, S., & Ali, S. S. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123471 · Cited by 23
Synthesized Speech Detection Using Convolutional Transformer-Based Spectrogram Analysis
Bartusiak, E. R., & Delp, E. J. (2021) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf53345.2021.9723142 · Cited by 20
Self-citationSDHF: Spotting DeepFakes with Hierarchical Features
Liang, T., Chen, P., Zhou, G., Gao, H., Liu, J., Li, Z., & Dai, J. (2020) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai50040.2020.00108 · Cited by 15
DeepFake Videos Detection by Using Recurrent Neural Network (RNN)
Albazony, A. A. M., Alwzwazy, H. A., Al-Khaleefa, A. S., Alazzawi, M. A., Almohamadi, M., & Alavi, S. E. (2023) · Al-Sadiq International Conference on Communication and Information Technology (AICCIT) · doi:10.1109/aiccit57614.2023.10217956 · Cited by 15
Fake-checker: A fusion of texture features and deep learning for deepfakes detection
Huda, N. U., Javed, A., Maswadi, K., Alhazmi, A. H., & Ashraf, R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17586-x · Cited by 16
Scalable Universal Adversarial Watermark Defending Against Facial Forgery
Qiao, T., Zhao, B., Shi, R., Han, M., Hassaballah, M., Retraint, F., & Luo, X. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3460387 · Cited by 15
Adversarial Attack on Deepfake Detection Using RL Based Texture Patches
Fernandes, S. L., & Jha, S. K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66415-2_14 · Cited by 15
Javed, M., Han, D., Dahri, F. H., Kumar, T., Bhutto, J. A., & Khowaja, A. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01188-1 · Cited by 15
UAM‐Net : Robust Deepfake Detection Through Hybrid Attention Into Scalable Convolutional Network
Sudarshana, K., & Yendapalli, V. (2025) · Expert Systems · doi:10.1111/exsy.70009 · Cited by 17
Ahmad, W., Ali, I., Shahzad, S. A., Hashmi, A., & Ghaffar, F. (2022) · International journal of electrical and computer engineering systems · doi:10.32985/ijeces.13.9.9 · Cited by 11
DeepExplain: Enhancing DeepFake Detection Through Transparent and Explainable AI model
Sunkari, V., & Srinagesh, A. (2024) · Informatica · doi:10.31449/inf.v48i8.5792 · Cited by 21
SWYNT: Swin Y-Net Transformers for Deepfake Detection
Khalid, F., Akbar, M. H., & Gul, S. (2023) · International Conference on Robotics and Automation in Industry (ICRAI) · doi:10.1109/icrai57502.2023.10089585 · Cited by 13
Detecting Deepfakes with Deep Learning and Gabor Filters
Hadi, W. J., Kadhem, S. M., & Abbas, A. R. (2022) · ARO-The Scientific Journal of Koya University · doi:10.14500/aro.10917 · Cited by 10
Deepfake Detection using Multi-path CNN and Convolutional Attention Mechanism
Rineesh Babu, P., & Nair, P. (. ). M. S. (2022) · IEEE Mysore Sub Section International Conference (MysuruCon) · doi:10.1109/mysurucon55714.2022.9972657 · Cited by 10
DoFNet: Depth of Field Difference Learning for Detecting Image Forgery
Jeong, Y., Choi, J., Kim, D., Park, S., Hong, M., Park, C., Min, S.-J., & Gwon, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69544-6_6 · Cited by 11
Changing landscape of fake news research on social media: a bibliometric analysis
Sahid, A., Maleh, Y., & Ouazzane, K. (2025) · Quality & Quantity · doi:10.1007/s11135-024-02048-9 · Cited by 10
Gupta, V., Srivastava, V., Yadav, A. K., Vishwakarma, D. K., & Kumar, N. (2025) · Applied Intelligence · doi:10.1007/s10489-024-06168-5 · Cited by 9
A Visual Computing Unified Application Using Deep Learning and Computer Vision Techniques
Sowmya, B. J., Meeradevi, M., Seema, S., Dayananda, P., Supreeth, S., Shruthi, G., & Rohith, S. (2024) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v18i01.42673 · Cited by 9
Aggregating Layers for Deepfake Detection
Jevnisek, A., & Avidan, S. (2022) · 2022 26th International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr56361.2022.9956052 · Cited by 5
Fused Swish-ReLU Efficient-Net Model for Deepfakes Detection
Ilyas, H., Javed, A., Aljasem, M., & Alhababi, M. (2023) · International Conference on Automation, Robotics and Applications · doi:10.1109/icara56516.2023.10125801 · Cited by 9
Alrawahneh, A. A.-M., Abdullah, S. N. H. S., Abu-Ain, T., Abdullah, S. N. A. S., Taylor, S. K., & Suhaimi, N. H. S. (2025) · IEEE Access · doi:10.1109/access.2025.3569792 · Cited by 9
Metadata
- Authors
- Montserrat, D. M., Hao, H., Yarlagadda, S. K., Baireddy, S., Shao, R., Horvath, J., Bartusiak, E., Yang, J., Guera, D., Zhu, F., & Delp, E. J.
- Affiliations
- Purdue University West Lafayette (US)
- ORCID
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 2851-2859
- OpenAlex
- W3020392129
- Cited by
- 181
- References
- 41
- Citations by year
- 2026: 18 · 2025: 23 · 2024: 39 · 2023: 37 · 2022: 42 · 2021: 13 · 2020: 9
- FWCI
- 7.79
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Face recognition and analysis
- Keywords
- deepfake detection, video manipulation, DFDC, convolutional neural networks, recurrent neural networks, temporal features, visual features, social media misinformation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
