{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T04:24:00Z","timestamp":1743567840995,"version":"3.40.3"},"publisher-location":"Cham","reference-count":78,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031861482","type":"print"},{"value":"9783031861499","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-86149-9_18","type":"book-chapter","created":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T22:19:09Z","timestamp":1743545949000},"page":"182-193","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Survey on Technologies of Video Deepfake Detection"],"prefix":"10.1007","author":[{"given":"Chanchan","family":"Li","sequence":"first","affiliation":[]},{"given":"Zuyi","family":"Song","sequence":"additional","affiliation":[]},{"given":"Yutong","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Xu\u2019an","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,1]]},"reference":[{"key":"18_CR1","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. In: International Conference on Neural Information Processing Systems, pp. 2672\u20132680. MIT Press (2014)"},{"key":"18_CR2","unstructured":"Deepfakes. https:\/\/github.com\/deepfakes\/faceswap"},{"key":"18_CR3","unstructured":"Faceswap. https:\/\/github.com\/MarekKowalski\/FaceSwap"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Thies, J., Zollh\u00f6fer, M., Stamminger, M., et al.: Face2Face: real-time face capture and reenactment of RGB video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2387\u20132395 (2016)","DOI":"10.1109\/CVPR.2016.262"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Thies, J., Zollh\u00f6fer, M., Niessner, M.: Deferred neural rendering: image synthesis using neural textures. ACM Trans. Graph. 38(4), 66:1\u201366:12 (2019)","DOI":"10.1145\/3306346.3323035"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Korshunova, I., Shi, W., Dambre, J., et al.: Fast face-swap using convolutional neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3677\u20133685 (2017)","DOI":"10.1109\/ICCV.2017.397"},{"key":"18_CR7","unstructured":"Ulyanov, D., Lebedev, V., Vedaldi, A., et al.: Texture networks: feed-forward synthesis of textures and stylized images. In: International Conference on Machine Learning, pp. 1349\u20131357. PMLR (2016)"},{"key":"18_CR8","unstructured":"Faceswap-Gan. https:\/\/github.com\/shaoanlu\/faceswap-GAN"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2242\u20132251. IEEE (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Li, Q., Wang, J., et al.: One shot face swapping on megapixels. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, pp. 4834\u20134844. Computer Vision Foundation\/IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.00480"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., et al.: Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8107\u20138116. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"18_CR12","doi-asserted-by":"publisher","unstructured":"Luo, Y., et al.: StyleFace: towards identity-disentangled face generation on megapixels. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13676, pp. 297\u2013312. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19787-1_17","DOI":"10.1007\/978-3-031-19787-1_17"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Xu, C., Zhang, J., Hua, M., et al.: Region-aware face swapping. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7622\u20137631. IEEE (2022)","DOI":"10.1109\/CVPR52688.2022.00748"},{"key":"18_CR14","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., et al.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5967\u20135976. IEEE Computer Society (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Nirkin, Y., Keller, Y., Hassner, T.: FSGAN: subject agnostic face swapping and re-enactment. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7183\u20137192. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00728"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Ren, Y., Li, G., Chen, Y., et al.: PIRenderer: controllable portrait image generation via semantic neural rendering. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13739\u201313748. IEEE (2021)","DOI":"10.1109\/ICCV48922.2021.01350"},{"key":"18_CR17","doi-asserted-by":"publisher","unstructured":"Yin, F., et al.: StyleHEAT: one-shot high-resolution editable talking face generation via pre-trained stylegan. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13677, pp. 85\u2013101. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19790-1_6","DOI":"10.1007\/978-3-031-19790-1_6"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4401\u20134410. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Pang, Y., Zhang, Y., Quan, W., et al.: DPE: disentanglement of pose and expression for general video portrait editing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 427\u2013436 (2023)","DOI":"10.1109\/CVPR52729.2023.00049"},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Johnson, M.K., Farid, H.: Exposing digital forgeries by detecting inconsistencies in lighting. In: Proceedings of the 7th Workshop on Multimedia and Security, pp. 1\u201310. ACM (2005)","DOI":"10.1145\/1073170.1073171"},{"issue":"10","key":"18_CR21","doi-asserted-by":"crossref","first-page":"3948","DOI":"10.1109\/TSP.2005.855406","volume":"53","author":"AC Popescu","year":"2005","unstructured":"Popescu, A.C., Farid, H.: Exposing digital forgeries in color filter array interpolated images. IEEE Trans. Sig. Process. 53(10), 3948\u20133959 (2005)","journal-title":"IEEE Trans. Sig. Process."},{"issue":"2","key":"18_CR22","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1109\/TIFS.2006.873602","volume":"1","author":"J Lukas","year":"2006","unstructured":"Lukas, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1(2), 205\u2013214 (2006)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"18_CR23","doi-asserted-by":"crossref","unstructured":"Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW), pp. 83\u201392. IEEE (2019)","DOI":"10.1109\/WACVW.2019.00020"},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8261\u20138265. IEEE (2019)","DOI":"10.1109\/ICASSP.2019.8683164"},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8261\u20138265. IEEE (2019)","DOI":"10.1109\/ICASSP.2019.8683164"},{"key":"18_CR26","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"18_CR27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778. IEEE Computer Society (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"18_CR28","doi-asserted-by":"crossref","unstructured":"Bayar, B., Stamm, M.C.: A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, pp. 5\u201310. ACM (2016)","DOI":"10.1145\/2909827.2930786"},{"key":"18_CR29","doi-asserted-by":"crossref","unstructured":"Zhou, P., Han, X., Morariu, V.I., et al.: Two-stream neural networks for tampered face detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1831\u201318399. IEEE (2017)","DOI":"10.1109\/CVPRW.2017.229"},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"R\u00f6ssler, A., Cozzolino, D., Verdoliva, L., et al.: FaceForensics++: learning to detect manipulated facial images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1\u201311. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00009"},{"key":"18_CR31","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800\u20131807. IEEE Computer Society (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"18_CR32","doi-asserted-by":"crossref","unstructured":"Dang, H., Liu, F., Stehouwer, J., et al.: On the detection of digital face manipulation. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5780\u20135789. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00582"},{"key":"18_CR33","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhou, W., Chen, D., et al.: Multi-attentional deepfake detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2185\u20132194. Computer Vision Foundation\/IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"18_CR34","doi-asserted-by":"publisher","unstructured":"Sun, K., et al.: An information theoretic approach for attention-driven face forgery detection. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13674, pp. 111\u2013127. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19781-9_7","DOI":"10.1007\/978-3-031-19781-9_7"},{"key":"18_CR35","doi-asserted-by":"crossref","unstructured":"Liu, Z., Qi, X., Torr, P.H.S.: Global texture enhancement for fake face detection in the wild. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8057\u20138066. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00808"},{"key":"18_CR36","doi-asserted-by":"crossref","unstructured":"Wang, C., Deng, W.: Representative forgery mining for fake face detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14923\u201314932. Computer Vision Foundation\/IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.01468"},{"key":"18_CR37","doi-asserted-by":"publisher","unstructured":"Liang, J., Shi, H., Deng, W.: Exploring disentangled content information for face forgery detection. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13674, pp. 128\u2013145. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19781-9_8","DOI":"10.1007\/978-3-031-19781-9_8"},{"key":"18_CR38","doi-asserted-by":"crossref","unstructured":"Li, L., Bao, J., Zhang, T., et al.: Face X-ray for more general face forgery detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5000\u20135009. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"18_CR39","doi-asserted-by":"crossref","unstructured":"Dong, X., Bao, J., Chen, D., et al.: Protecting celebrities from deepfake with identity consistency transformer. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9458\u20139468. IEEE (2022)","DOI":"10.1109\/CVPR52688.2022.00925"},{"key":"18_CR40","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, pp. 5998\u20136008 (2017)"},{"key":"18_CR41","doi-asserted-by":"crossref","unstructured":"Nguyen, H.H., Fang, F., Yamagishi, J., et al.: Multi-task learning for detecting and segmenting manipulated facial images and videos. In: 10th IEEE International Conference on Biometrics Theory, Applications and Systems (BTAS 2019), pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/BTAS46853.2019.9185974"},{"key":"18_CR42","doi-asserted-by":"publisher","unstructured":"Wang, X., Yao, T., Ding, S., Ma, L.: Face manipulation detection via auxiliary supervision. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds.) ICONIP 2020. LNCS, vol. 12532, pp. 313\u2013324. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-63830-6_27","DOI":"10.1007\/978-3-030-63830-6_27"},{"key":"18_CR43","doi-asserted-by":"crossref","unstructured":"Jiang, L., Li, R., Wu, W., et al.: 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, pp. 2886\u20132895. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00296"},{"key":"18_CR44","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., et al.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489\u20134497. IEEE Computer Society (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"18_CR45","doi-asserted-by":"publisher","unstructured":"Wang, L., et al.: Temporal segment networks: towards good practices for deep action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNIP, vol. 9912, pp. 20\u201336. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_2","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"18_CR46","doi-asserted-by":"crossref","unstructured":"Carreira, J., Zisserman, A.: Quo Vadis, action recognition? A new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4724\u20134733. IEEE Computer Society (2017)","DOI":"10.1109\/CVPR.2017.502"},{"issue":"8","key":"18_CR47","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"18_CR48","doi-asserted-by":"crossref","unstructured":"Hu, Z., Xie, H., Wang, Y., et al.: Dynamic inconsistency-aware deepfake video detection. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI), pp. 736\u2013742 (2021)","DOI":"10.24963\/ijcai.2021\/102"},{"key":"18_CR49","doi-asserted-by":"crossref","unstructured":"Li, X., Lang, Y., Chen, Y., et al.: Sharp multiple instance learning for deepfake video detection. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 1864\u20131872. ACM (2020)","DOI":"10.1145\/3394171.3414034"},{"key":"18_CR50","doi-asserted-by":"publisher","unstructured":"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. LNIP, vol. 12352, pp. 667\u2013684. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58571-6_39","DOI":"10.1007\/978-3-030-58571-6_39"},{"key":"18_CR51","doi-asserted-by":"crossref","unstructured":"Gu, Z., Chen, Y., Yao, T., et al.: Spatiotemporal inconsistency learning for deepfake video detection. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3473\u20133481. ACM (2021)","DOI":"10.1145\/3474085.3475508"},{"key":"18_CR52","doi-asserted-by":"crossref","unstructured":"Gu, Z., Chen, Y., Yao, T., et al.: Delving into the local: dynamic inconsistency learning for deepfake video detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 1, pp. 744\u2013752 (2022)","DOI":"10.1609\/aaai.v36i1.19955"},{"key":"18_CR53","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Bao, J., Chen, D., et al.: Exploring temporal coherence for more general video face forgery detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15024\u201315034. IEEE (2021)","DOI":"10.1109\/ICCV48922.2021.01477"},{"key":"18_CR54","doi-asserted-by":"crossref","unstructured":"Li, Y., Chang, M.C., Lyu, S.: In ictu oculi: exposing AI generated fake face videos by detecting eye blinking. arXiv e-prints arXiv:1806.02877 (2018)","DOI":"10.1109\/WIFS.2018.8630787"},{"key":"18_CR55","doi-asserted-by":"crossref","unstructured":"Haliassos, A., Vougioukas, K., Petridis, S., et al.: Lips don\u2019t lie: a generalizable and robust approach to face forgery detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5039\u20135049. Computer Vision Foundation\/IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.00500"},{"key":"18_CR56","doi-asserted-by":"crossref","unstructured":"Qi, H., Guo, Q., Juefei-Xu, F., et al.: DeepRhythm: exposing deepfakes with attentional visual heartbeat rhythms. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 4318\u20134327. ACM (2020)","DOI":"10.1145\/3394171.3413707"},{"key":"18_CR57","doi-asserted-by":"crossref","unstructured":"Durall, R., Keuper, M., Keuper, J.: Watch your up-convolution: CNN based generative deep neural networks are failing to reproduce spectral distributions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7887\u20137896. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00791"},{"key":"18_CR58","doi-asserted-by":"crossref","unstructured":"Wang, S.Y., Wang, O., Zhang, R., et al: CNN-generated images are surprisingly easy to spot... for now. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8692\u20138701. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00872"},{"key":"18_CR59","doi-asserted-by":"publisher","unstructured":"Qian, Y., Yin, G., Sheng, L., Chen, Z., Shao, J.: Thinking in frequency: face forgery detection by mining frequency-aware clues. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) ECCV 2020. LNIP, vol. 12357, pp. 86\u2013103. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58610-2_6","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"18_CR60","doi-asserted-by":"crossref","unstructured":"Chen, S., Yao, T., Chen, Y., et al.: Local relation learning for face forgery detection. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, 2\u20139 February 2021, vol. 35, no. 2, 1081\u20131088. AAAI Press (2021)","DOI":"10.1609\/aaai.v35i2.16193"},{"key":"18_CR61","doi-asserted-by":"crossref","unstructured":"Li, J., Xie, H., Li, J., et al.: Frequency-aware discriminative feature learning supervised by single-center loss for face forgery detection. In: V\/IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6458\u20136467. Computer Vision Foundation\/IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.00639"},{"key":"18_CR62","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhang, Y., Yan, J., et al.: Generalizing face forgery detection with high-frequency features. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16317\u201316326. Computer Vision Foundation\/IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.01605"},{"key":"18_CR63","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, X., Zhou, W., et al.: Spatial-phase shallow learning: rethinking face forgery detection in frequency domain. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 772\u2013781. Computer Vision Foundation\/IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.00083"},{"key":"18_CR64","doi-asserted-by":"crossref","unstructured":"Li, J., Xie, H., Yu, L., et al.: Wavelet-enhanced weakly supervised local feature learning for face forgery detection. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 1299\u20131308. ACM (2022)","DOI":"10.1145\/3503161.3547832"},{"key":"18_CR65","unstructured":"Korshunov, P., Marcel, S.: DeepFakes: a new threat to face recognition? Assessment and detection, 14 September 2022. https:\/\/arxiv.org\/abs\/1812.08685"},{"key":"18_CR66","unstructured":"DeepFake Detection. DeepFake Detection github, 14 September 2022. https:\/\/github.com\/ondyari\/FaceForensics"},{"key":"18_CR67","unstructured":"Dolhansky, B., Howes, R., Pflaum, B., et al.: The deepfake detection challenge (DFDC) preview dataset [EB\/OL], 14 September 2022. https:\/\/arxiv.org\/abs\/1910.08854"},{"key":"18_CR68","doi-asserted-by":"crossref","unstructured":"Jiang, L.M., Li, R., Wu, W., et al.: DeeperForensics-1.0: a large-scale dataset for real-world face forgery detection. In: Proceedings of 2020IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2886\u20132895. IEEE, Seattle (2020)","DOI":"10.1109\/CVPR42600.2020.00296"},{"key":"18_CR69","unstructured":"Li, Y.Z., Yang, X., Sun, P., et al.: Celeb-DF (v2): a new dataset for deepfake forensics [EB\/OL], 14 September 2022. https:\/\/arxiv.org\/abs\/1909.12962"},{"key":"18_CR70","doi-asserted-by":"crossref","unstructured":"Zi, B.J., Chang, M.H., Chen, J.J., et al.: WildDeepfake: a challenging real-world dataset for deepfake detection. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2382\u20132390. ACM, Seattle (2020)","DOI":"10.1145\/3394171.3413769"},{"key":"18_CR71","doi-asserted-by":"crossref","unstructured":"Zhou, T.F., Wang, W.G., Liang, Z.Y., et al.: Face forensics in the wild. In: Proceedings of 2021IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5774\u20135784. IEEE, Nashville (2021)","DOI":"10.1109\/CVPR46437.2021.00572"},{"key":"18_CR72","doi-asserted-by":"crossref","unstructured":"He, Y.N., Gan, B., Chen, S.Y., et al.: ForgeryNet: a versatile benchmark for comprehensive forgery analysis. In: Proceedings of 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4358\u20134367. IEEE, Nashville (2021)","DOI":"10.1109\/CVPR46437.2021.00434"},{"key":"18_CR73","doi-asserted-by":"crossref","unstructured":"Le, T.N., Nguyen, H.H., Yamagishi, J., et al.: OpenForensics: large-scale challenging dataset for multi-face forgery detection and segmentation in-the-wild. In: Proceedings of 2021 IEEE\/CVF International Conference on Computer Vision, Montreal, Canada, pp. 10097\u201310107. IEEE (2021)","DOI":"10.1109\/ICCV48922.2021.00996"},{"key":"18_CR74","unstructured":"Sanderson, C.: The VidTIMIT database 14 September 2022. https:\/\/conradsanderson.id.au\/vidtimit\/"},{"key":"18_CR75","unstructured":"Dolhansky, B., Bitton, J., Pflaum, B., et al.: The deepfake detection challenge (DFDC) dataset, 14 September 2022. https:\/\/arxiv.org\/abs\/2006.07397"},{"key":"18_CR76","doi-asserted-by":"crossref","unstructured":"Chung, J.S., Nagrani, A., Zisserman, A.: VoxCeleb2: deep speaker recognition. In: Proceedings of the 19th Annual Conference of the International Speech Communication Association, ISCA 2018, Hyderabad, India, pp. 1086\u20131090 (2018)","DOI":"10.21437\/Interspeech.2018-1929"},{"key":"18_CR77","unstructured":"Khalid, H., Tariq, S., Kim, M., et al.: FakeAVCeleb: a novel audio-video multimodal deepfake dataset. In: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (Round 2). Curran Associates, Inc. (2021)"},{"key":"18_CR78","doi-asserted-by":"crossref","unstructured":"Cai, Z.X., Stefanov, K., Dhall, A., et al.: Do you really mean that? Content driven audio-visual deepfake dataset and multimodal method for temporal forgery localization, 14 September 2022. https:\/\/arxiv.org\/abs\/2204.06228","DOI":"10.1109\/DICTA56598.2022.10034605"}],"container-title":["Lecture Notes on Data Engineering and Communications Technologies","Advances in Internet, Data and Web Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-86149-9_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T22:19:45Z","timestamp":1743545985000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-86149-9_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031861482","9783031861499"],"references-count":78,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-86149-9_18","relation":{},"ISSN":["2367-4512","2367-4520"],"issn-type":[{"value":"2367-4512","type":"print"},{"value":"2367-4520","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EIDWT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Emerging Internet, Data & Web Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Matsue","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 February 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 February 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eidwt12025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/voyager.ce.fit.ac.jp\/conf\/eidwt\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}