| Title | Hybrid Recurrent Deep Learning Model for DeepFake Video Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Jaiswal, Gaurav |
| Conference Name | 2021 IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) |
| Keywords | Computational modeling, Deep Learning, deep video, DeepFake, deepfake detection, DeepFake Video, Hybrid Recurrent model, Metrics, Nonhomogeneous media, privacy, pubcrawl, resilience, Resiliency, Scalability, security, Stacking, Task Analysis, video temporal feature |
| Abstract | Nowadays deepfake videos are concern with social ethics, privacy and security. Deepfake videos are synthetically generated videos that are generated by modifying the facial features and audio features to impose one person’s facial data and audio to other videos. These videos can be used for defaming and fraud. So, counter these types of manipulations and threats, detection of deepfake video is needed. This paper proposes multilayer hybrid recurrent deep learning models for deepfake video detection. Proposed models exploit the noise-based temporal facial convolutional features and temporal learning of hybrid recurrent deep learning models. Experiment results of these models demonstrate its performance over stacked recurrent deep learning models. |
| DOI | 10.1109/UPCON52273.2021.9667632 |
| Citation Key | jaiswal_hybrid_2021 |
Hybrid Recurrent Deep Learning Model for DeepFake Video Detection