Development of ML-Based Solution for Detection of Deepfake Face-Swap Videos

Deepfake technology, driven by advancements in deep learning and generative models, enables highly realistic manipulation of facial appearances in videos, often through face-swapping techniques. While such methods have potential in entertainment and creative applications, they also pose serious threats to privacy, trust, and information integrity. This paper presents the development of a machine learning (ML)-based system for detecting face-swap deepfake videos. The proposed approach employs video preprocessing, frame extraction, and facial region isolation, followed by feature extraction using a deep convolutional neural network (ResNeXt). Temporal consistency is analyzed with a Long Short-Term Memory (LSTM) network to capture sequential artifacts. Experimental results demonstrate the system’s ability to distinguish real and fake videos with high accuracy, contributing to digital forensics and misinformation mitigation efforts.[1]

Deepfake detection, Face-swap videos

1. Goodfellow, I., et al. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27. [Google Scholar] [Crossref]

2. Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. (2018). MesoNet: a compact facial video forgery detection network. IEEE WIFS, 1-7. [Google Scholar] [Crossref]

3. Li, Y., Chang, M.C., & Lyu, S. (2018). In Ictu Oculi: Exposing AI created fake videos by detecting eye blinking. IEEE International Workshop on Information Forensics and Security. [Google Scholar] [Crossref]

4. Rossler, A., et al. (2019). FaceForensics++: Learning to detect manipulated facial images. ICCV, 1-11. [Google Scholar] [Crossref]

5. Durall, R., Keuper, M., Pfreundt, F.J., & Keuper, J. (2020). Watch your up-convolution: CNN-based generative deep neural networks are failing to reproduce spectral distributions. CVPR. [Google Scholar] [Crossref]

6. Dolhansky, B., et al. (2020). The DeepFake Detection Challenge Dataset. arXiv preprint arXiv:2006.07397. [Google Scholar] [Crossref]

7. Padmanabhuni, S.S., Gera, P., Reddy, A.M. Hybrid leaf generative adversarial networks scheme for classification of tomato leaves-early blight disease healthy, Advancement of Deep Learning and Its Applications in Object Detection and Recognition, 2022, pp. 2б1–281 [Google Scholar] [Crossref]

8. Srinivasa Reddy, K., Rao, P.V., Reddy, A.M., ... Narayana, J.L., Silpapadmanabhuni, S. neural network aided optimized auto encoder and decoder for detection of covid-1e and pneumonia using ct-scan, Journal of Theoretical and Applied Information Technology, 2022, 100(21), pp. б34б–б3б0 [Google Scholar] [Crossref]

9. aik, S., Kamidi, D., Govathoti, S., Cheruku, R., Mallikarjuna Reddy, A. Eficient diabetic retinopathy detection using convolutional neural network and data augmentation, Soft Computing, 2023. [Google Scholar] [Crossref]

10. Chintha, V.V.R., Ayaluri, M.R. A Review Paper on IoT Solutions in Health Sector, Proceedings of International Conference on Applied Innovation in IT, 2023, 11(1), pp. 221–225 [Google Scholar] [Crossref]

11. Manoranjan Dash et al.,” Effective Automated Medical Image Segmentation Using Hybrid Computational Intelligence Technique”, Blockchain and IoT Based Smart Healthcare Systems, Bentham Science Publishers, Pp. 174-182,2024 [Google Scholar] [Crossref]

12. Umur Aybars Ciftci, ˙Ilke Demir, Lijun Yin “Detection of Synthetic Portrait Videos using Biological Signals” in arXiv:1901.02212v2 [Google Scholar] [Crossref]

13. D. Güera and E. J. Delp, "Deepfake Video Detection Using Recurrent Neural Networks," 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland, New Zealand, 2018, pp. 1-6. [Google Scholar] [Crossref]

14. Manoranjan Dash, “Modified VGG-16 model for COVID-19 chest X-ray images: optimal binary severity assessment,” International Journal of Data Mining and Bioinformatics, vol. 1, no. 1, Jan. 2025, doi: 10.1504/ijdmb.2025.10065665. [Google Scholar] [Crossref]

15. Huy H. Nguyen , Junichi Yamagishi, and Isao Echizen “Using capsule networks to detect forged images and videos” in arXiv:1810.11215 [Google Scholar] [Crossref]

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