# iCaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection
## 5 Discussion  
In this research, a new deepfake detection model (iCaps-Dfake) is proposed that combined texture feature extraction with HRNet based method to train a capsule network to improve the classification accuracy. The proposed model results show great improvement over other state-of-the-art methods. The selection of YOLO-v3 as a face detector led to great reduction in false positive faces predictions thus reducing the need for further preprocessing steps. The fusion between two different in nature feature extraction approaches enriched the model and helped it learn better. The texture-based approach using the LBP provided an artifacts-specific flavor to our model by counting on the difference between textures of the fake face and the surrounding background. The CNN-based using HRNet provided our capsule network with informative-representations while preserving the capsule concept of not losing any spatial information to get the most out of small details. The augmentation-normalization step performed on images that were input to the HRNet helped in enhancing the results. The CapsNet with its advantageous performance in feature abstraction and classification tasks made a robust decision whether the introduced image is fake or real. Our results were compared to other sophisticated models and showed not only an improvement in the AUC but also a generalization capability of the proposed model. For DFDC-P, it achieved an average 12.12% improvement in the frame-level score and 3.4% at the video-level score. For Celeb-DF, the proposed model attained an average 18.01% increase in frame-level score and 14.35% increase in video-level score. It also scored an average 26.91% increase in frame-level and 20.25% increase in video-level when further training the model on Celeb-DF. In the future, a plan is made to expand the model such that it can also handle fake audio embeddings and try to achieve even higher results. Conceptualization, S.N.S. ; Data curation, S.S.K. ; Formal analysis, S.S.K. and S.N.S. ; Investigation, S.N.S. ; Methodology, S.M.Y. ; Project administration, S.M.Y. ; Resources, S.S.K. ; Software, S.S.K. ; Supervision, S.M.Y. and S.N.S. ; Validation, S.S.K., S.M.Y. and S.N.S. ; Visualization, S.S.K. ; Writing--Original draft, S.S.K. ; Writing--Review & Editing, S.M.Y. and S.N.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study during the collecting of DFDC-P dataset while Celeb-DF dataset was collected from public YouTube videos so no consents are obtained. This research received no external funding. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON] _Synthesizing Obama: Learning Lip Sync from Audio_; ACM Transactions on Graphics (ToG): New York, NY, USA, 2017; Volume 36, pp. 1-13. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] _Deep Video Portraits_; ACM Transactions on Graphics (TOG): New York, NY, USA, 2018; Volume 37, pp. 1-14. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] Everybody dance now. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 2 November 2019. * AI Can Now Create Fake Porn (2021) AI Can Now Create Fake Porn, Making Revenge Porn Even More Complicated. The Conversation. Available online: [[https://theconversation.com/ai-can-now-create-fake-porn-making-revenge-porn-even-more-complicated-92267](https://theconversation.com/ai-can-now-create-fake-porn-making-revenge-porn-even-more-complicated-92267)]([https://theconversation.com/ai-can-now-create-fake-porn-making-revenge-porn-even-more-complicated-92267](https://theconversation.com/ai-can-now-create-fake-porn-making-revenge-porn-even-more-complicated-92267)) (accessed on 9 February 2021). * You Thought Fake News Was Bad? Deep Fakes Are Where Truth Goes to Die. The Guardian.