Mobile QR Code QR CODE

2025

Reject Ratio

81.5%

References

1 
Negi S. , Jayachandran M. , Upadhyay S. , 2021, Deep fake: An understanding of fake images and videos, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol. 7, No. 3, pp. 183-189DOI
2 
Ivanov N. S. , Arzhskov A. V. , Ivanenko V. G. , 2020, Combining deep learning and super-resolution algorithms for deep fake detection, Proc. of IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, pp. 326-328DOI
3 
He P. , Li H. , Li B. , Wang H. , Liu L. , 2020, Exposing fake bitrate videos using hybrid deep-learning network from recompression error, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 30, No. 11, pp. 4034-4049DOI
4 
Suratkar S. , Bhiungade S. , Pitale J. , Soni K. , Badgujar T. , Kazi F. , 2023, Deep-fake video detection approaches using convolutional–recurrent neural networks, Journal of Control and Decision, Vol. 10, No. 2, pp. 198-214DOI
5 
Agarwal S. , Farid H. , Fried O. , Agrawala M. , 2020, Detecting deep-fake videos from phoneme-viseme mismatches, IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 2814-2822DOI
6 
Pashine S. , Mandiya S. , Gupta P. , Sheikh R. , 2021, Deep fake detection: Survey of facial manipulation detection solutions, International Research Journal of Engineering and Technology, Vol. 8, No. 5, pp. 4441-4449DOI
7 
Hao X. , Li M. , 2021, Deepfake video detection based on 3D convolutional neural networks, Computer Science, Vol. 48, No. 7, pp. 86-92DOI
8 
Wang Y. , Dantcheva A. , 2020, A video is worth more than 1000 lies: Comparing 3DCNN approaches for detecting deepfakes, Proc. of IEEE International Conference on Automatic Face and Gesture Recognition, pp. 515-519DOI
9 
Beaulieu A. , Thullier F. , Bouchard K. , Maître J. , Gaboury S. , 2022, Ultra-wideband data as input of a combined EfficientNet and LSTM architecture for human activity recognition, Journal of Ambient Intelligence and Smart Environments, Vol. 14, No. 3, pp. 157-172DOI
10 
Shi Y. , Ma Z. , Chen H. , Ke Y. , Chen Y. , Zhou X. , 2024, High-resolution recognition of FOAM modes via an improved EfficientNet V2 based convolutional neural network, Frontiers of Physics, Vol. 19, No. 3, pp. 32205DOI
11 
Zhao S. , Nguyen T. H. , Ma B. , 2021, Monaural speech enhancement with complex convolutional block attention module and joint time-frequency losses, IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6648-6652DOI
12 
Rössler A. , Cozzolino D. , Verdoliva L. , Riess C. , Thies J. , Niessner M. , 2019, FaceForensics++: Learning to detect manipulated facial images, Proc. of IEEE/CVF International Conference on Computer Vision, pp. 1-11DOI
13 
Dolhansky B. , Howes R. , Pflaum B. , Baram N. , Ferrer C. C. , 2019, The deepfake detection challenge (DFDC) preview dataset, arXiv preprint arXiv:1910.08854DOI
14 
Li Y. , Yang X. , Sun P. , Qi H. , Lyu S. , 2020, Celeb-DF: A large-scale challenging dataset for deepfake forensics, Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3204-3213DOI
15 
Zhang K. , Zhang Z. , Li Z. , Qiao Y. , 2016, Joint face detection and alignment using multitask cascaded convolutional networks, IEEE Signal Processing Letters, Vol. 23, No. 10, pp. 1499-1503DOI
16 
Afchar D. , Nozick V. , Yamagishi J. , Echizen I. , 2018, MesoNet: A compact facial video forgery detection network, Proc. of IEEE International Workshop on Information Forensics and Security, pp. 1-7DOI
17 
Chollet F. , 2017, Xception: Deep learning with depthwise separable convolutions, Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1800-1807DOI
18 
Montserrat D. M. , Hao H. , Yarlagadda S. K. , Baireddy S. , Shao R. , Horváth J. , Bartusiak E. , Uang J. , Güera D. , Zhu R. , Delp E. J. , 2020, Deepfakes detection with automatic face weighting, IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 2851-2859DOI