DeepFake Detection using 3D-Xception Net with Discrete Fourier Transformation
Subject Areas : Image ProcessingAdeep Biswas 1 , Debayan Bhattacharya 2 , Kakelli Anil Kumar 3 *
1 - School of Computer Science and Engineering, Vellore Institute of Technology, Vellore
2 - School of Computer Science and Engineering, Vellore Institute of Technology, Vellore
3 - Associate Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore
Keywords: Computer Vision, DeepFake Detection, Xception Net, Video Manipulation,
Abstract :
The videos are more popular for sharing content on social media to capture the audience’s attention. The artificial manipulation of videos is growing rapidly to make the videos flashy and interesting but they can easily misuse to spread false information on social media platforms. Deep Fake is a problematic method for the manipulation of videos in which artificial components are added to the video using emerging deep learning techniques. Due to the increase in the accuracy of deep fake generation methods, artificially created videos are no longer detectable and pose a major threat to social media users. To address this growing problem, we have proposed a new method for detecting deep fake videos using 3D Inflated Xception Net with Discrete Fourier Transformation. Xception Net was originally designed for application on 2D images only. The proposed method is the first attempt to use a 3D Xception Net for categorizing video-based data. The advantage of the proposed method is, it works on the whole video rather than the subset of frames while categorizing. Our proposed model was tested on the popular dataset Celeb-DF and achieved better accuracy.
[1] Kumar, P., Vatsa, M., & Singh, R. Detecting face2face facial reenactment in videos. In The IEEE Winter Conference on Applications of Computer Vision, IEEE, 2020, pp. 2589-2597.
[2] Sabir, E., Cheng, J., Jaiswal, A., AbdAlmageed, W., Masi, I., & Natarajan, P. Recurrent convolutional strategies for face manipulation detection in videos. Interfaces (GUI), 2019, vol 3(1).
[3] Nguyen, T. T., Nguyen, C. M., Nguyen, D. T., Nguyen, D. T., & Nahavandi, S. Deep learning for deepfakes creation and detection, 2019, arXiv preprint arXiv:1909.11573.
[4] Lyu, S. Deepfake detection: Current challenges and next steps. In 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), IEEE, 2019, pp. 1-6.
[5] Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. Deepfakes and beyond: A survey of face manipulation and fake detection. 2020, arXiv preprint arXiv:2001.00179.
[6] Bitouk, D., Kumar, N., Dhillon, S., Belhumeur, P., & Nayar, S. K. Face swapping: automatically replacing faces in photographs. In ACM SIGGRAPH 2008 papers, 2008, pp. 1-8.
[7] Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., & Nießner, M. Face2face: Real-time face capture and reenactment of rgb videos. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016 pp. 2387-2395.
[8] Tolosana, R., Romero-Tapiador, S., Fierrez, J., & Vera-Rodriguez, R. DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance, 2020, arXiv preprint arXiv:2004.07532.
[9] Li, Y., Yang, X., Sun, P., Qi, H., & Lyu, S. Celeb-df: A new dataset for deepfake forensics, 2019, arXiv preprint arXiv:1909.12962.
[10] Chollet, F. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1251-1258.
[11] Huang, Y., Juefei-Xu, F., Wang, R., Xie, X., Ma, L., Li, J., ... & Pu, G. FakeLocator: Robust Localization of GAN-Based Face Manipulations via Semantic Segmentation Networks with Bells and Whistles, 2020, arXiv preprint arXiv:2001.09598.
[12] Nirkin, Y., Keller, Y., & Hassner, T. FSGAN: Subject agnostic face swapping and reenactment. In Proceedings of the IEEE international conference on computer vision, 2019, pp. 7184-7193.
[13] McCloskey, S., & Albright, M. Detecting gan-generated imagery using color cues, 2018, arXiv preprint arXiv:1812.08247.
[14] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., & Guo, B. Face x-ray for more general face forgery detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 5001-5010.
[15] Amerini, I., Galteri, L., Caldelli, R., & Del Bimbo, A. Deepfake video detection through optical flow based cnn. In Proceedings of the IEEE International Conference on Computer Vision Workshops, 2019.
[16] Fernando, T., Fookes, C., Denman, S., & Sridharan, S. Exploiting human social cognition for the detection of fake and fraudulent faces via memory networks. 2019, arXiv preprint arXiv:1911.07844.
[17] Venkatesh, S., Ramachandra, R., Raja, K., Spreeuwers, L., Veldhuis, R., & Busch, C. Detecting morphed face attacks using residual noise from deep multi-scale context aggregation network. In The IEEE Winter Conference on Applications of Computer Vision, 2020, pp. 280-289.
[18] Jeon, H., Bang, Y., & Woo, S. S. FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network, 2020, arXiv preprint arXiv:2001.01265.
[19] Zhang, W., Zhao, C., & Li, Y. A Novel Counterfeit Feature Extraction Technique for Exposing Face-Swap Images Based on Deep Learning and Error Level Analysis. Entropy, 2020, vol 22(2), no. 249.
[20] Dang, L. M., Min, K., Lee, S., Han, D., & Moon, H. Tampered and computer-generated face images identification based on deep learning. Applied Sciences, 2020, vol 10(2), no. 505.
[21] Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Nießner, M. Faceforensics: A large-scale video dataset for forgery detection in human faces, 2018, arXiv preprint arXiv:1803.09179.
[22] Jiang, L., Li, R., Wu, W., Qian, C., & Loy, C. C. DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 2889-2898.
[23] de Lima, O., Franklin, S., Basu, S., Karwoski, B., & George, A. Deepfake Detection using Spatiotemporal Convolutional Networks, 2020, arXiv preprint arXiv:2006.14749.
[24] Li, Y., Yang, X., Sun, P., Qi, H., & Lyu, S. Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 3207-3216.
[25] Chollet, F. Xception: deep learning with depthwise separable convolutions, in: 2017 IEEE conference on computer vision and pattern recognition CVPR, 2017.
[26] Kaiser, L., Gomez, A. N., & Chollet, F. Depthwise separable convolutions for neural machine translation, 2017, arXiv preprint arXiv:1706.03059.
[27] Rahimian, E., Zabihi, S., Atashzar, S. F., Asif, A., & Mohammadi, A. XceptionTime: Independent Time-Window Xceptiontime Architecture for Hand Gesture Classification. In ICASSP IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2020, pp. 1304-1308.
[28] Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. Deepfakes and beyond: A survey of face manipulation and fake detection, 2020, arXiv preprint arXiv:2001.00179.
[29] Guarnera, L., Giudice, O., & Battiato, S. DeepFake Detection by Analyzing Convolutional Traces. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, pp. 666-667.
[30] Zhang, J., Salehizadeh, M., & Diller, E. Parallel pick and place using two independent untethered mobile magnetic microgrippers in IEEE International Conference on Robotics and Automation, 2018.
[31] Bau, D., Zhu, J. Y., Strobelt, H., Zhou, B., Tenenbaum, J. B., Freeman, W. T., & Torralba, A. Visualizing and understanding generative adversarial networks, 2019, arXiv preprint arXiv:1901.09887.
[32] Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. Generative adversarial networks: An overview. IEEE Signal Processing Magazine, vol 35(1), 2018, pp 53-65.
[33] Kietzmann, J., Lee, L. W., McCarthy, I. P., & Kietzmann, T. C. Deepfakes: Trick or treat?. Business Horizons, 2020, vol 63(2), pp 135-146.
[34] Wang, J., Liu, A., & Xiao, J. Video-Based Pig Recognition with Feature-Integrated Transfer Learning. In Chinese Conference on Biometric Recognition, Springer, Cham, 2018, pp 620-631.
[35] Güera, D., & Delp, E. J. Deepfake video detection using recurrent neural networks. In 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE,2018, pp. 1-6.
[36] Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. Mesonet: a compact facial video forgery detection network. In IEEE International Workshop on Information Forensics and Security (WIFS), IEEE, 2018, pp. 1-7.
[37] Sohrawardi, S. J., Chintha, A., Thai, B., Seng, S., Hickerson, A., Ptucha, R., & Wright, M. Poster: Towards robust open-world detection of deepfakes. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019, pp. 2613-2615.
[38] Albahar, M., & Almalki, J. Deepfakes: Threats and countermeasures systematic review. Journal of Theoretical and Applied Information Technology, vol 97(22), 2019, pp 3242-3250.
[39] Maksutov, A. A., Morozov, V. O., Lavrenov, A. A., & Smirnov, A. S. Methods of Deepfake Detection Based on Machine Learning. In 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), IEEE, 2020, pp. 408-411.
[40] Korshunov, P., & Marcel, S. Deepfakes: a new threat to face recognition assessment and detection, 2018, arXiv preprint arXiv:1812.08685.