Real-Time Advanced Computational Intelligence for Deep Fake Video Detection
Source abstract
Abstract
As digitization is increasing, threats to our data are also increasing at a faster pace. Generating fake videos does not require any particular type of knowledge, hardware, memory, or any computational device; however, its detection is challenging. Several methods in the past have solved the issue, but computation costs are still high and a highly efficient model has yet to be developed. Therefore, we proposed a new model architecture known as DFN (Deep Fake Network), which has the basic blocks of mobNet, a linear stack of separable convolution, max-pooling layers with Swish as an activation function, and XGBoost as a classifier to detect deepfake videos. The proposed model is more accurate compared to Xception, Efficient Net, and other state-of-the-art models. The DFN performance was tested on a DFDC (Deep Fake Detection Challenge) dataset. The proposed method achieved an accuracy of 93.28% and a precision of 91.03% with this dataset. In addition, training and validation loss was 0.14 and 0.17, respectively. Furthermore, we have taken care of all types of facial manipulations, making the model more robust, generalized, and lightweight, with the ability to detect all types of facial manipulations in videos.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Media Forensic Detection · Generative Adversarial Networks and Image Synthesis · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Engineering, National Institute of Technology, Kurukshetra 136119, India
Department College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin 39524, Saudi Arabia
Department of Computer Engineering & Applications, GLA University, Mathura 281406, India
Department School of Computing, Graphic Era Hill University, Dehradun 248002, India
Department of Computer Science and Engineering, Graphic Era Deemed to be University Dehradun, Uttarakhand 248002, India
References (45)
Show source-provided references
1. Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial networks. arXiv.
2. (2020, November 10). Faceswap: Deepfakes Software for All. Available online: https://github.com/deepfakes/faceswap.
3. Deepfakes: Trick or treat?10.1016/j.bushor.2019.11.006 ↗
4. Synthesizing obama: Learning lip sync from audio
5. (2020, October 28). FakeApp 2.2.0. Available online: https://www.malavida.com/en/soft/fakeapp/.
6. (2020, October 02). Bloomberg (11 September 2018). How Faking Videos Became Easy and Why That’s So Scary. Available online: https://fortune.com/2018/09/11/deep-fakes-obama-video/.
7. Deepfakes and the new disinformation war: The coming age of post-truth geopolitics
8. Patrick, T. (2023, January 03). The Newest AI-Enabled Weapon: Deep-Faking Photos of the Earth. Defense One. Available online: https://www.defenseone.com/technology/2019/03/next-phase-ai-deep-faking-whole-world-and-china-ahead/155944/.
9. Kumar, A., Dadheech, P., and Chaudhary, U. (2020, January 7–8). Energy conservation in WSN: A review of current techniques. Proceedings of the 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), Jaipur, India.10.1109/ICETCE48199.2020.9091736 ↗
10. Wider or deeper: Revisiting the resnet model for visual recognition10.1016/j.patcog.2019.01.006 ↗
11. François, C. (2017, January 21–26). Xception: Deep learning with depthwise separable convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.
12. Human burn depth and grafting prognosis using ResNeXt topology based deep learning network10.1007/s11042-022-12555-2 ↗
13. Sense understanding of text conversation using temporal convolution neural network10.1007/s11042-022-12090-0 ↗
14. Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets10.1007/s12530-022-09426-4 ↗
15. A robust framework for glaucoma detection using CLAHE and EfficientNet10.1007/s00371-021-02114-5 ↗
16. Deepfakes and beyond: A survey of face manipulation and fake detection10.1016/j.inffus.2020.06.014 ↗
17. Ismail, A., Elpeltagy, M., Zaki, M.S., and Eldahshan, K. (2021). A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost. Sensors, 21.10.3390/s21165413 ↗
18. Deep fake image detection based on pairwise learning10.3390/app10010370 ↗
19. Sumit, C., Hadsell, R., and LeCun, Y. (2005, January 20–26). Learning a similarity metric discriminatively, with application to face verification. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA.
20. Mingxing, T., and Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv.
21. Li, Y., and Lyu, S. (2018). Exposing deepfake videos by detecting face warping artifacts. arXiv.
22. The creation and detection of deepfakes: A survey
23. David, G., and Delp, E.J. (2018, January 27–30). Deepfake video detection using recurrent neural networks. Proceedings of the 15th IEEE International Conference on Advanced Video and Signal-BASED Surveillance (AVSS), Auckland, New Zealand.
24. DeepVision: Deepfakes Detection Using Human Eye Blinking Pattern10.1109/ACCESS.2020.2988660 ↗
25. Thi, N.T., Nguyen, C.M., Nguyen, D.T., Nguyen, D.T., and Nahavandi, S. (2019). Deep learning for deepfakes creation and detection. arXiv.
26. Sergey, I., and Szegedy, C. (2015, January 6–11). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France.
27. ADD: Attention-Based DeepFake Detection Approach10.3390/bdcc5040049 ↗
28. Montserrat, D.M., Hao, H., Yarlagadda, S.K., Baireddy, S., Shao, R., Horvath, J., Bartusiak, E., Yang, J., Guera, D., and Zhu, F. (2020, January 14–19). Deepfakes Detection with Automatic Face Weighting. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.10.1109/CVPRW50498.2020.00342 ↗
29. A Survey on Deepfake Video Detection10.1049/bme2.12031 ↗
30. Exposing DeepFake Videos Using Attention Based Convolutional LSTM Network10.1007/s11063-021-10588-6 ↗
31. Wodajo, D., and Atnafu, S. (2021). Deep fake video detection using convolutional vision transformer. arXiv.
32. Bonettini, N., Cannas, E.D., Mandelli, S., Bondi, L., Bestagini, P., and Tubaro, S. (2021, January 10–15). Video face manipulation detection through ensemble of CNNs. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.10.1109/ICPR48806.2021.9412711 ↗
33. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You only look once: Unifified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
34. YOLO-Face: A Real-Time Face Detector10.1007/s00371-020-01831-7 ↗
35. Kumar, R., Arora, R., Bansal, V., Sahayasheela, V.J., Buckchash, H., Imran, J., Narayanan, N., Pandian, G.N., and Raman, B. (2020). Accurate prediction of COVID-19 using chest X-ray images through deep feature learning model with SMOTE and machine learning classififiers. MedRxiv.10.1101/2020.04.13.20063461 ↗
36. Distance based pattern driven mining for outlier detection in high dimensional big dataset
37. Kumar, A., Dadheech, P., Singh, V., and Raja, L. (2021). Data Deduplication Approaches, Academic Press.
38. Afchar, D., Nozick, V., Yamagishi, J., and Echizen, I. (2018, January 11–13). Mesonet: A compact facial video forgery detection network. Proceedings of the 2018 IEEE International Workshop on Information Forensics and Security (WIFS), Hong Kong, China.10.1109/WIFS.2018.8630761 ↗
39. Kumar, A., Dadheech, P., Beniwal, M.K., Agarwal, B., and Patidar, P.K. A fuzzy logic-based control system for detection and mitigation of blackhole attack in vehicular Ad Hoc network. Microservices in Big Data Analytics: Proceedings of the Second International, ICETCE 2019, Rajasthan, India, 1–2 February 2019.10.1007/978-981-15-0128-9_15 ↗
40. Yang, X., Li, Y., and Lyu, S. (2019, January 12–17). Exposing deep fakes using inconsistent head poses. Proceedings of the ICASSP 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.10.1109/ICASSP.2019.8683164 ↗
41. Charitidis, P., Kordopatis-Zilos, G., Papadopoulos, S., and Kompatsiaris, I. (2020). A face preprocessing approach for improved deepfake detection. arXiv.
42. Kumar, A., Bhavsar, A., and Verma, R. (2020, January 29–30). Detecting deepfakes with metric learning. Proceedings of the 2020 8th International Workshop on Biometrics and Forensics (IWBF), Porto, Portugal.10.1109/IWBF49977.2020.9107962 ↗
43. Li, Y., Chang, M.C., and Lyu, S. (2018, January 11–13). In ictu oculi: Exposing ai created fake videos by detecting eye blinking. Proceedings of the 2018 IEEE International Workshop on Information Forensics and Security (WIFS), Hong Kong, China.10.1109/WIFS.2018.8630787 ↗
44. Deepfake Detection in Digital Media Forensics10.1016/j.gltp.2022.04.017 ↗
45. Deepfake Detection: A Systematic Literature Review10.1109/ACCESS.2022.3154404 ↗