Abstract
Recently, the abuse of face swapping technique including Deepfakes has garnered widespread attention. Facial manipulation techniques arise serious problem, including information confusion and erosion of trust. Previous methods for detecting deepfakes have primarily focused on identifying forged traces in independent frames or from two-dimensional face. As a result, the challenge of detecting forged traces from video sequence and higher dimensions has emerged as a critical research area. In this paper, we approach face swapping detection from a novel perspective by converting face frames into three-dimensional space. We argue that the Euclidean distance between landmarks in 2D faces and 3D facial reprojections, along with the shape and texture feature of the 3D face model, serve as clues to discern authenticity. Our framework incorporates a Recurrent Neural Network (RNN) to effectively exploit temporal features from videos. Extensive experiments conducted on several datasets demonstrate the effectiveness of our method. Particularly in cross-manipulation experiments, our approach outperforms state-of-the-art competitors, highlighting its potential as a robust solution for detecting deepfakes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: MesoNet: a compact facial video forgery detection network. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–7. IEEE (2018)
Amerini, I., Galteri, L., Caldelli, R., Del Bimbo, A.: DeepFake video detection through optical flow based CNN. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (2019)
Bulat, A., Tzimiropoulos, G.: How far are we from solving the 2D & 3D face alignment problem? (and a dataset of 230,000 3D facial landmarks). In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1021–1030 (2017)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Deng, Y., Yang, J., Xu, S., Chen, D., Jia, Y., Tong, X.: Accurate 3D face reconstruction with weakly-supervised learning: from single image to image set. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)
Dong, X., et al.: Protecting celebrities from DeepFake with identity consistency transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9468–9478 (2022)
Gu, Z., et al.: Spatiotemporal inconsistency learning for DeepFake video detection. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3473–3481 (2021)
Guo, Y., Cai, J., Jiang, B., Zheng, J., et al.: CNN-based real-time dense face reconstruction with inverse-rendered photo-realistic face images. IEEE Trans. Pattern Anal. Mach. Intell. 41(6), 1294–1307 (2018)
Haliassos, A., Vougioukas, K., Petridis, S., Pantic, M.: Lips don’t lie: a generalisable and robust approach to face forgery detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5039–5049 (2021)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, B., et al.: Implicit identity driven DeepFake face swapping detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4490–4499 (2023)
Li, L., Bao, J., Yang, H., Chen, D., Wen, F.: FaceShifter: towards high fidelity and occlusion aware face swapping. arXiv preprint arXiv:1912.13457 (2019)
Li, X., et al.: Sharp multiple instance learning for DeepFake video detection. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 1864–1872 (2020)
Li, Y., Chang, M.C., Lyu, S.: In Ictu Oculi: exposing AI created fake videos by detecting eye blinking. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–7. IEEE (2018)
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, pp. 3207–3216 (2020)
Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16317–16326 (2021)
Masi, I., Killekar, A., Mascarenhas, R.M., Gurudatt, S.P., AbdAlmageed, W.: Two-branch recurrent network for isolating deepfakes in videos. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020, Part VII. LNCS, vol. 12352, pp. 667–684. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58571-6_39
Nguyen, H.H., Yamagishi, J., Echizen, I.: Capsule-forensics: using capsule networks to detect forged images and videos. In: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2019, pp. 2307–2311. IEEE (2019)
Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Nießner, M.: FaceForensics++: learning to detect manipulated facial images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1–11 (2019)
Sohrawardi, S.J., et al.: Poster: towards robust open-world detection of deepfakes. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 2613–2615 (2019)
Sun, K., Yao, T., Chen, S., Ding, S., Li, J., Ji, R.: Dual contrastive learning for general face forgery detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 2316–2324 (2022)
Sun, Z., Han, Y., Hua, Z., Ruan, N., Jia, W.: Improving the efficiency and robustness of deepfakes detection through precise geometric features. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3609–3618 (2021)
Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489–4497 (2015)
Zhang, D., Li, C., Lin, F., Zeng, D., Ge, S.: Detecting deepfake videos with temporal dropout 3DCNN. In: IJCAI, pp. 1288–1294 (2021)
Zhang, D., Lin, F., Hua, Y., Wang, P., Zeng, D., Ge, S.: Deepfake video detection with spatiotemporal dropout transformer. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 5833–5841 (2022)
Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., Li, S.Z.: S3FD: single shot scale-invariant face detector. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 192–201 (2017)
Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2185–2194 (2021)
Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.G.: WildDeepfake: a challenging real-world dataset for deepfake detection. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2382–2390 (2020)
Acknowledgements
This work was supported by the National Key R &D Program of China (2021YFF0602101) and National Natural Science Foundation of China (62172227).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Cheng, Z., Chen, C., Zhou, Y., Hu, X. (2024). Mining Temporal Inconsistency with 3D Face Model for Deepfake Video Detection. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14431. Springer, Singapore. https://doi.org/10.1007/978-981-99-8540-1_19
Download citation
DOI: https://doi.org/10.1007/978-981-99-8540-1_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8539-5
Online ISBN: 978-981-99-8540-1
eBook Packages: Computer ScienceComputer Science (R0)