{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T16:32:42Z","timestamp":1793377962513,"version":"4.5.0"},"reference-count":120,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,10,14]],"date-time":"2023-10-14T00:00:00Z","timestamp":1697241600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,14]],"date-time":"2023-10-14T00:00:00Z","timestamp":1697241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-1629898"],"award-info":[{"award-number":["CNS-1629898"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s00371-023-02981-0","type":"journal-article","created":{"date-parts":[[2023,10,14]],"date-time":"2023-10-14T16:01:38Z","timestamp":1697299298000},"page":"2733-2750","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Deepfake source detection in a heart beat"],"prefix":"10.1007","volume":"40","author":[{"given":"Umur Aybars","family":"\u00c7ift\u00e7i","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4177-0311","authenticated-orcid":false,"given":"\u0130lke","family":"Demir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,14]]},"reference":[{"issue":"4","key":"2981_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3306346.3323035","volume":"38","author":"J Thies","year":"2019","unstructured":"Thies, J., Zollh\u00f6fer, M., Nie\u00dfner, M.: Deferred neural rendering: image synthesis using neural textures. ACM Trans. Graph. 38(4), 1\u201312 (2019)","journal-title":"ACM Trans. Graph."},{"key":"2981_CR2","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: The IEEE conference on computer vision and pattern recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"2981_CR3","unstructured":"Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for improved quality, stability, and variation. CoRR abs\/1710.10196 (2017) arXiv:1710.10196"},{"key":"2981_CR4","doi-asserted-by":"crossref","unstructured":"Wang, S.-Y., Wang, O., Owens, A., Zhang, R., Efros, A.A.: Detecting photoshopped faces by scripting photoshop. In: The IEEE international conference on computer vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.01017"},{"key":"2981_CR5","unstructured":"FaceSwap. https:\/\/github.com\/MarekKowalski\/FaceSwap. Accessed: 2020-03-16"},{"key":"2981_CR6","unstructured":"DeepFakes. https:\/\/github.com\/deepfakes\/faceswap. Accessed: 2020-03-16"},{"key":"2981_CR7","unstructured":"FakeApp. https:\/\/www.malavida.com\/en\/soft\/fakeapp\/. Accessed: 2020-03-16"},{"key":"2981_CR8","unstructured":"Deepfakes are being used to dub adverts into different languages. https:\/\/www.newscientist.com\/article\/2220628-deepfakes-are-being-used-to-dub-adverts-into-different-languages\/. Accessed: 2020-05-27"},{"key":"2981_CR9","unstructured":"Here\u2019s Harrison Ford Starring in \u2019Solo\u2019 Thanks to Deepfakes. https:\/\/www.popularmechanics.com\/culture\/movies\/ a23867069\/harrison-ford-han-solo-deepfakes\/. Accessed: 2020-05-27"},{"key":"2981_CR10","unstructured":"Are Deepfakes the future of advertising? https:\/\/gritdaily.com\/deepfakes-in-advertising\/. Accessed: 2020-05-27"},{"issue":"8","key":"2981_CR11","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1109\/TMM.2013.2280560","volume":"15","author":"M Yuan","year":"2013","unstructured":"Yuan, M., Khan, I.R., Farbiz, F., Yao, S., Niswar, A., Foo, M.: A mixed reality virtual clothes try-on system. IEEE Trans Multimed 15(8), 1958\u20131968 (2013)","journal-title":"IEEE Trans Multimed"},{"key":"2981_CR12","unstructured":"Deepfake technology in the entertainment industry: potential, limitations and protections. https:\/\/amt-lab.org\/blog\/2020\/3\/deepfake-technology-in-the-entertainment-industry-potential-limitations-and-protections. Accessed: 2020-05-27"},{"key":"2981_CR13","unstructured":"Lawmakers warn of \u2019deepfake\u2019 videos ahead of 2020 election. https:\/\/www.cnn.com\/2019\/01\/28\/tech\/deepfake-lawmakers\/index.html. Accessed: 2020-05-27"},{"key":"2981_CR14","unstructured":"Deepfake porn nearly ruined my life. https:\/\/www.elle.com\/uk\/life-and-culture\/a30748079\/ deepfake-porn\/. Accessed: 2020-05-27"},{"key":"2981_CR15","unstructured":"Chu, D., Demir, \u0130., Eichensehr, K., Foster, J.G., Green, M.L., Lerman, K., Menczer, F., O\u2019Connor, C., Parson, E., Ruthotto, L., et al.: White paper: Deep fakery \u2013 an action plan. Technical Report http:\/\/www.ipam.ucla.edu\/wp-content\/uploads\/2020\/01\/Whitepaper-Deep-Fakery.pdf, Institute for pure and applied mathematics (IPAM), University of California Los Angeles, Los Angeles, CA (January 2020)"},{"key":"2981_CR16","doi-asserted-by":"crossref","unstructured":"Yadav, D., Salmani, S.: Deepfake: a survey on facial forgery technique using generative adversarial network. In: 2019 International conference on intelligent computing and control systems (ICCS), pp. 852\u2013857 (2019)","DOI":"10.1109\/ICCS45141.2019.9065881"},{"key":"2981_CR17","doi-asserted-by":"publisher","first-page":"84003","DOI":"10.1109\/ACCESS.2019.2924733","volume":"7","author":"MS Nadeem","year":"2019","unstructured":"Nadeem, M.S., Franqueira, V.N.L., Zhai, X., Kurugollu, F.: A survey of deep learning solutions for multimedia visual content analysis. IEEE Access 7, 84003\u201384019 (2019)","journal-title":"IEEE Access"},{"key":"2981_CR18","doi-asserted-by":"publisher","unstructured":"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\u20137 (2018). https:\/\/doi.org\/10.1109\/WIFS.2018.8630787","DOI":"10.1109\/WIFS.2018.8630787"},{"issue":"5","key":"2981_CR19","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1007\/s00371-020-01849-x","volume":"37","author":"Y Sun","year":"2021","unstructured":"Sun, Y., Xiong, H., Yiu, S.M.: Understanding deep face anti-spoofing: from the perspective of data. The Visual Computer 37(5), 1015\u20131028 (2021)","journal-title":"The Visual Computer"},{"key":"2981_CR20","doi-asserted-by":"publisher","first-page":"2461","DOI":"10.1007\/s00371-021-02123-4","volume":"38","author":"S Arora","year":"2022","unstructured":"Arora, S., Bhatia, M., Mittal, V.: A robust framework for spoofing detection in faces using deep learning. Vis. Comput. 38, 2461\u20132472 (2022)","journal-title":"Vis. Comput."},{"key":"2981_CR21","doi-asserted-by":"publisher","unstructured":"\u00c7ift\u00e7i, U.A., Demir, \u0130., Yin, L.: How do the hearts of deep fakes beat? deep fake source detection via interpreting residuals with biological signals. In: 2020 IEEE International joint conference on biometrics (IJCB), pp. 1\u201310 (2020). https:\/\/doi.org\/10.1109\/IJCB48548.2020.9304909","DOI":"10.1109\/IJCB48548.2020.9304909"},{"issue":"7","key":"2981_CR22","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1109\/TBME.2016.2609282","volume":"64","author":"W Wang","year":"2017","unstructured":"Wang, W., den Brinker, A.C., Stuijk, S., de Haan, G.: Algorithmic principles of remote ppg. IEEE Trans Biomed Eng 64(7), 1479\u20131491 (2017)","journal-title":"IEEE Trans Biomed Eng"},{"key":"2981_CR23","doi-asserted-by":"publisher","unstructured":"McDuff, D.J., Estepp, J.R., Piasecki, A.M., Blackford, E.B.: A survey of remote optical photoplethysmographic imaging methods. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp. 6398\u20136404 (2015). https:\/\/doi.org\/10.1109\/EMBC.2015.7319857","DOI":"10.1109\/EMBC.2015.7319857"},{"key":"2981_CR24","doi-asserted-by":"crossref","unstructured":"\u00c7ift\u00e7i, U.A., Yin, L.: Heart rate based face synthesis for pulse estimation. In: ISVC (2019)","DOI":"10.1007\/978-3-030-33720-9_42"},{"key":"2981_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3142993","author":"M Chen","year":"2022","unstructured":"Chen, M., Liao, X., Wu, M.: Pulseedit: editing physiological signals in facial videos for privacy protection. IEEE Trans Inf Forensics Secur (2022). https:\/\/doi.org\/10.1109\/TIFS.2022.3142993","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"2981_CR26","doi-asserted-by":"crossref","unstructured":"Conotter, V., Bodnari, E., Boato, G., Farid, H.: Physiologically-based detection of computer generated faces in video. In: 2014 IEEE International conference on image processing (ICIP), pp. 248\u2013252 (2014)","DOI":"10.1109\/ICIP.2014.7025049"},{"key":"2981_CR27","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3009287","author":"UA \u00c7ift\u00e7i","year":"2020","unstructured":"\u00c7ift\u00e7i, U.A., Demir, \u0130, Yin, L.: FakeCatcher: detection of synthetic portrait videos using biological signals. IEEE Trans Pattern Anal Mach Intell (PAMI) (2020). https:\/\/doi.org\/10.1109\/TPAMI.2020.3009287","journal-title":"IEEE Trans Pattern Anal Mach Intell (PAMI)"},{"key":"2981_CR28","doi-asserted-by":"crossref","unstructured":"Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP 2019 - 2019 IEEE International conference on acoustics, speech and signal processing (ICASSP), pp. 8261\u20138265 (2019)","DOI":"10.1109\/ICASSP.2019.8683164"},{"key":"2981_CR29","doi-asserted-by":"crossref","unstructured":"Hsiao C (1996) Logit and Probit Models. Springer, Dordrecht (1996)","DOI":"10.1007\/978-94-009-0137-7_16"},{"key":"2981_CR30","doi-asserted-by":"crossref","unstructured":"Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Niessner, M.: Faceforensics++: Learning to detect manipulated facial images. In: The IEEE International conference on computer vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00009"},{"key":"2981_CR31","unstructured":"Khalid, H., Tariq, S., Kim, M., Woo, S.S.: Fakeavceleb: A novel audio-video multimodal deepfake dataset (2021) arXiv:2108.05080 [cs.CV]"},{"key":"2981_CR32","doi-asserted-by":"crossref","unstructured":"Li, Y., Sun, P., Qi, H., Lyu, S.: Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics. In: IEEE conference on computer vision and patten recognition (CVPR), Seattle, WA, United States (2020)","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"2981_CR33","unstructured":"R\u00f6ssler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Nie\u00dfner, M.: FaceForensics: A large-scale video dataset for forgery detection in human faces. arXiv e-prints, 1803\u201309179 (2018) arXiv:1803.09179 [cs.CV]"},{"key":"2981_CR34","doi-asserted-by":"publisher","DOI":"10.1145\/3425780","author":"Y Mirsky","year":"2021","unstructured":"Mirsky, Y., Lee, W.: The creation and detection of deepfakes: a survey. ACM Comput. Surv. (2021). https:\/\/doi.org\/10.1145\/3425780","journal-title":"ACM Comput. Surv."},{"key":"2981_CR35","doi-asserted-by":"crossref","unstructured":"Thies, J., Zollh\u00f6fer, M., Stamminger, M., Theobalt, C., Nie\u00dfner, M.: Face2Face: Real-time Face capture and reenactment of RGB videos. In: Proceeding computer vision and pattern recognition (CVPR), IEEE (2016)","DOI":"10.1109\/CVPR.2016.262"},{"key":"2981_CR36","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818056","author":"J Thies","year":"2015","unstructured":"Thies, J., Zollh\u00f6fer, M., Nie\u00dfner, M., Valgaerts, L., Stamminger, M., Theobalt, C.: Real-time expression transfer for facial reenactment. ACM Trans. Graph. (2015). https:\/\/doi.org\/10.1145\/2816795.2818056","journal-title":"ACM Trans. Graph."},{"key":"2981_CR37","doi-asserted-by":"crossref","unstructured":"Huang, Y., Juefei-Xu, F., Wang, R., Guo, Q., Ma, L., Xie, X., Li, J., Miao, W., Liu, Y., Pu, G.: Fakepolisher: Making deepfakes more detection-evasive by shallow reconstruction. In: Proceedings of the 28th ACM International conference on multimedia (2020)","DOI":"10.1145\/3394171.3413732"},{"key":"2981_CR38","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., Choo, J.: Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2018)","DOI":"10.1109\/CVPR.2018.00916"},{"key":"2981_CR39","unstructured":"Demir, I., Ciftci, U.A.: Mixsyn: Learning composition and style for multi-source image synthesis. arXiv preprint arXiv:2111.12705 (2021)"},{"key":"2981_CR40","doi-asserted-by":"crossref","unstructured":"Nirkin, Y., Masi, I., Tran Tuan, A., Hassner, T., Medioni, G.: On face segmentation, face swapping, and face perception. In: 2018 13th IEEE International conference on automatic face gesture recognition (FG 2018), pp. 98\u2013105 (2018)","DOI":"10.1109\/FG.2018.00024"},{"key":"2981_CR41","doi-asserted-by":"crossref","unstructured":"Garrido, P., Valgaerts, L., Rehmsen, O., Thormahlen, T., Perez, P., Theobalt, C.: Automatic face reenactment. In: The IEEE conference on computer vision and pattern recognition (CVPR) (2014)","DOI":"10.1109\/CVPR.2014.537"},{"key":"2981_CR42","doi-asserted-by":"crossref","unstructured":"Wu, W., Zhang, Y., Li, C., Qian, C., Change\u00a0Loy, C.: Reenactgan: Learning to reenact faces via boundary transfer. In: The European conference on computer vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01246-5_37"},{"key":"2981_CR43","doi-asserted-by":"crossref","unstructured":"Prajwal, K., Mukhopadhyay, R., Namboodiri, V., Jawahar, C.: A lip sync expert is all you need for speech to lip generation in the wild. In: Proceedings of the 28th ACM International conference on multimedia, pp. 484\u2013492 (2020)","DOI":"10.1145\/3394171.3413532"},{"key":"2981_CR44","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.inffus.2020.06.014","volume":"64","author":"R Tolosana","year":"2020","unstructured":"Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., Ortega-Garcia, J.: Deepfakes and beyond: a survey of face manipulation and fake detection. Inf Fus 64, 131\u2013148 (2020)","journal-title":"Inf Fus"},{"key":"2981_CR45","doi-asserted-by":"crossref","unstructured":"Masi, I., Killekar, A., Mascarenhas, R.M., Gurudatt, S.P., AbdAlmageed, W.: Two-branch recurrent network for isolating Deepfakes in videos. arXiv e-prints, 2008\u201303412 (2020) arXiv:2008.03412 [cs.CV]","DOI":"10.1007\/978-3-030-58571-6_39"},{"key":"2981_CR46","doi-asserted-by":"publisher","unstructured":"Pan, D., Sun, L., Wang, R., Zhang, X., Sinnott, R.O.: Deepfake detection through deep learning. In: 2020 IEEE\/ACM International conference on big data computing, applications and technologies (BDCAT), pp. 134\u2013143 (2020). https:\/\/doi.org\/10.1109\/BDCAT50828.2020.00001","DOI":"10.1109\/BDCAT50828.2020.00001"},{"issue":"9","key":"2981_CR47","doi-asserted-by":"publisher","first-page":"4990","DOI":"10.1002\/int.22499","volume":"36","author":"J Liu","year":"2021","unstructured":"Liu, J., Zhu, K., Lu, W., Luo, X., Zhao, X.: A lightweight 3d convolutional neural network for deepfake detection. Int J Intell Syst 36(9), 4990\u20135004 (2021)","journal-title":"Int J Intell Syst"},{"key":"2981_CR48","doi-asserted-by":"crossref","unstructured":"Sebyakin A, Soloviev VAZ (2021) Spatio-temporal deepfake detection with deep neural networks. In: Toeppe K, Yan H, Chu SKW (eds.) Diversity Divergence Dialogue. iConference Lecture Notes in Computer Science. Springer, Cham","DOI":"10.1007\/978-3-030-71292-1_8"},{"key":"2981_CR49","doi-asserted-by":"publisher","unstructured":"Zhao, H., Wei, T., Zhou, W., Zhang, W., Chen, D., Yu, N.: Multi-attentional deepfake detection. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2185\u20132194. IEEE Computer Society, Los Alamitos, CA, USA (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.00222","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"2981_CR50","doi-asserted-by":"publisher","unstructured":"Amerini, I., Galteri, L., Caldelli, R., Del\u00a0Bimbo, A.: Deepfake video detection through optical flow based cnn. In: 2019 IEEE\/CVF International conference on computer vision workshop (ICCVW), pp. 1205\u20131207 (2019). https:\/\/doi.org\/10.1109\/ICCVW.2019.00152","DOI":"10.1109\/ICCVW.2019.00152"},{"key":"2981_CR51","doi-asserted-by":"publisher","unstructured":"Trinh, L., Tsang, M., Rambhatla, S., Liu, Y.: Interpretable and trustworthy deepfake detection via dynamic prototypes. In: 2021 IEEE Winter conference on applications of computer vision (WACV), pp. 1972\u20131982 (2021). https:\/\/doi.org\/10.1109\/WACV48630.2021.00202","DOI":"10.1109\/WACV48630.2021.00202"},{"key":"2981_CR52","doi-asserted-by":"publisher","unstructured":"Rana, M.S., Sung, A.H.: Deepfakestack: A deep ensemble-based learning technique for deepfake detection. In: 2020 7th IEEE International conference on cyber security and cloud computing (CSCloud)\/2020 6th IEEE International conference on edge computing and scalable cloud (EdgeCom), pp. 70\u201375 (2020). https:\/\/doi.org\/10.1109\/CSCloud-EdgeCom49738.2020.00021","DOI":"10.1109\/CSCloud-EdgeCom49738.2020.00021"},{"key":"2981_CR53","doi-asserted-by":"publisher","unstructured":"Lu, C., Liu, B., Zhou, W., Chu, Q., Yu, N.: Deepfake video detection using 3d-attentional inception convolutional neural network. In: 2021 IEEE International conference on image processing (ICIP), pp. 3572\u20133576 (2021). https:\/\/doi.org\/10.1109\/ICIP42928.2021.9506381","DOI":"10.1109\/ICIP42928.2021.9506381"},{"key":"2981_CR54","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-022-02732-7","author":"M Nawaz","year":"2022","unstructured":"Nawaz, M., Javed, A., Irtaza, A.: Resnet-swish-dense54: a deep learning approach for deepfakes detection. Vis Comput (2022). https:\/\/doi.org\/10.1007\/s00371-022-02732-7","journal-title":"Vis Comput"},{"key":"2981_CR55","unstructured":"Farid, H. The MIT Press essential knowledge series (2019)"},{"key":"2981_CR56","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Zheng, L., Thing, V.L.L.: Automated face swapping and its detection. In: 2017 IEEE 2nd International conference on signal and image processing (ICSIP), pp. 15\u201319 (2017). https:\/\/doi.org\/10.1109\/SIPROCESS.2017.8124497","DOI":"10.1109\/SIPROCESS.2017.8124497"},{"key":"2981_CR57","doi-asserted-by":"publisher","unstructured":"Zhou, P., Han, X., Morariu, V.I., Davis, L.S.: Two-stream neural networks for tampered face detection. In: 2017 IEEE Conference on computer vision and pattern recognition workshops (CVPRW), pp. 1831\u20131839 (2017). https:\/\/doi.org\/10.1109\/CVPRW.2017.229","DOI":"10.1109\/CVPRW.2017.229"},{"key":"2981_CR58","doi-asserted-by":"publisher","unstructured":"Khodabakhsh, A., Ramachandra, R., Raja, K., Wasnik, P., Busch, C.: Fake face detection methods: Can they be generalized? In: 2018 International conference of the biometrics special interest group (BIOSIG), pp. 1\u20136 (2018). https:\/\/doi.org\/10.23919\/BIOSIG.2018.8553251","DOI":"10.23919\/BIOSIG.2018.8553251"},{"key":"2981_CR59","doi-asserted-by":"crossref","unstructured":"Wang, S. Y., Wang, O., Zhang, R., Owens, A., Efros, A. A: CNN-generated images are surprisingly easy to spot... for now. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00872"},{"key":"2981_CR60","doi-asserted-by":"publisher","unstructured":"Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: 2020 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp. 5000\u20135009 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00505","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"2981_CR61","doi-asserted-by":"crossref","unstructured":"Huh, M., Liu, A., Owens, A., Efros, A.A.: Fighting fake news: Image splice detection via learned self-consistency. In: Proceedings of the European conference on computer vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01252-6_7"},{"key":"2981_CR62","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 39\u201357. IEEE computer society, Los Alamitos, CA, USA (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"2981_CR63","unstructured":"Saremsky, S.R., \u00c7ift\u00e7i, U.A., Greene, E.A., Demir, \u0130.: Adversarial deepfake generation for detector misclassification. In: IEEE\/CVF Conference on computer vision and pattern recognition (CVPR) Workshops (2022)"},{"key":"2981_CR64","doi-asserted-by":"publisher","unstructured":"Demir, \u0130., \u00c7ift\u00e7i, U.A.: Where do deep fakes look? synthetic face detection via gaze tracking. In: ACM Symposium on eye tracking research and applications. association for computing machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3448017.3457387","DOI":"10.1145\/3448017.3457387"},{"key":"2981_CR65","unstructured":"Li, Y., Lyu, S.: Exposing deepfake videos by detecting face warping artifacts. In: The IEEE Conference on computer vision and pattern recognition (CVPR) Workshops (2019)"},{"key":"2981_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2020.107616","volume":"174","author":"H Li","year":"2020","unstructured":"Li, H., Li, B., Tan, S., Huang, J.: Identification of deep network generated images using disparities in color components. Signal Proc 174, 107616 (2020)","journal-title":"Signal Proc"},{"key":"2981_CR67","doi-asserted-by":"publisher","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 (2019). https:\/\/doi.org\/10.1109\/WACVW.2019.00020","DOI":"10.1109\/WACVW.2019.00020"},{"key":"2981_CR68","doi-asserted-by":"publisher","unstructured":"Korshunov, P., Marcel, S.: Speaker inconsistency detection in tampered video. In: 2018 26th European signal processing conference (EUSIPCO), pp. 2375\u20132379 (2018). https:\/\/doi.org\/10.23919\/EUSIPCO.2018.8553270","DOI":"10.23919\/EUSIPCO.2018.8553270"},{"key":"2981_CR69","doi-asserted-by":"crossref","unstructured":"McCloskey, S., Albright, M.: Detecting gan-generated imagery using saturation cues. In: 2019 IEEE International conference on image processing (ICIP), pp. 4584\u20134588 (2019)","DOI":"10.1109\/ICIP.2019.8803661"},{"key":"2981_CR70","doi-asserted-by":"crossref","unstructured":"Yu, N., Davis, L.S., Fritz, M.: Attributing fake images to gans: Learning and analyzing gan fingerprints. In: The IEEE International conference on computer vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00765"},{"key":"2981_CR71","doi-asserted-by":"crossref","unstructured":"Roy, A., Bhalang\u00a0Tariang, D., Subhra\u00a0Chakraborty, R., Naskar, R.: Discrete cosine transform residual feature based filtering forgery and splicing detection in jpeg images. In: The IEEE Conference on computer vision and pattern recognition (CVPR) Workshops (2018)","DOI":"10.1109\/CVPRW.2018.00205"},{"key":"2981_CR72","doi-asserted-by":"publisher","unstructured":"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\u20137 (2018). https:\/\/doi.org\/10.1109\/WIFS.2018.8630761","DOI":"10.1109\/WIFS.2018.8630761"},{"key":"2981_CR73","doi-asserted-by":"publisher","unstructured":"Tariq, S., Lee, S., Kim, H., Shin, Y., Woo, S.S.: Detecting both machine and human created fake face images in the wild. In: Proceedings of the 2nd International workshop on multimedia privacy and security. MPS \u201918, pp. 81\u201387. ACM, New York, NY, USA (2018). https:\/\/doi.org\/10.1145\/3267357.3267367","DOI":"10.1145\/3267357.3267367"},{"key":"2981_CR74","doi-asserted-by":"publisher","unstructured":"G\u00fcera, 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), pp. 1\u20136 (2018). https:\/\/doi.org\/10.1109\/AVSS.2018.8639163","DOI":"10.1109\/AVSS.2018.8639163"},{"key":"2981_CR75","doi-asserted-by":"crossref","unstructured":"Nguyen, H.H., Yamagishi, J., Echizen, I.: Capsule-forensics: Using capsule networks to detect forged images and videos. In: ICASSP 2019 - 2019 IEEE International conference on acoustics, speech and signal processing (ICASSP), pp. 2307\u20132311 (2019)","DOI":"10.1109\/ICASSP.2019.8682602"},{"issue":"8","key":"2981_CR76","doi-asserted-by":"publisher","first-page":"1818","DOI":"10.1109\/TIFS.2016.2555286","volume":"11","author":"Z Boulkenafet","year":"2016","unstructured":"Boulkenafet, Z., Komulainen, J., Hadid, A.: Face spoofing detection using colour texture analysis. IEEE Trans Inf Forensics Secur 11(8), 1818\u20131830 (2016). https:\/\/doi.org\/10.1109\/TIFS.2016.2555286","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"2981_CR77","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.jvcir.2017.09.003","volume":"49","author":"M Barni","year":"2017","unstructured":"Barni, M., Bondi, L., Bonettini, N., Bestagini, P., Costanzo, A., Maggini, M., Tondi, B., Tubaro, S.: Aligned and non-aligned double jpeg detection using convolutional neural networks. J. Vis. Comun. Image Represent. 49, 153\u2013163 (2017)","journal-title":"J. Vis. Comun. Image Represent."},{"key":"2981_CR78","doi-asserted-by":"crossref","unstructured":"Qi, H., Guo, Q., Juefei-Xu, F., Xie, X., Ma, L., Feng, W., Liu, Y., Zhao, J.: Deeprhythm: Exposing deepfakes with a entional visual heartbeat rhythms. In: Proceedings of the 28th ACM International conference on multimedia (2020)","DOI":"10.1145\/3394171.3413707"},{"key":"2981_CR79","doi-asserted-by":"crossref","unstructured":"Straub, J.: Using subject face brightness assessment to detect \u2018deep fakes\u2019 (Conference Presentation). In: Kehtarnavaz, N., Carlsohn, M.F. (eds.) Real-Time Image Processing and Deep Learning 2019, vol. 10996 (2019). International Society for Optics and Photonics. https:\/\/doi.org\/10.1117\/12.2520546","DOI":"10.1117\/12.2520546"},{"key":"2981_CR80","unstructured":"Korshunov, P., Halstead, M., Castan, D., Graciarena, M., McLaren, M., Burns, B., Lawson, A., Marcel, S.: Tampered speaker inconsistency detection with phonetically aware audio-visual features. In: ICML Workshop \"Synthetic Realities: deep learning for detecting AudioVisual Fakes\" (2019). http:\/\/infoscience.epfl.ch\/record\/270130"},{"key":"2981_CR81","doi-asserted-by":"publisher","unstructured":"Le, N., Odobez, J.-M.: Learning multimodal temporal representation for dubbing detection in broadcast media. In: Proceedings of the 24th ACM International conference on multimedia. MM \u201916, pp. 202\u2013206. Association for Computing Machinery, New York, NY, USA (2016). https:\/\/doi.org\/10.1145\/2964284.2967211","DOI":"10.1145\/2964284.2967211"},{"key":"2981_CR82","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1109\/TIFS.2019.2916364","volume":"15","author":"D Cozzolino","year":"2020","unstructured":"Cozzolino, D., Verdoliva, L.: Noiseprint: a cnn-based camera model fingerprint. IEEE Trans Inf Forensics Secur 15, 144\u2013159 (2020)","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"2981_CR83","doi-asserted-by":"crossref","unstructured":"Marra, F., Gragnaniello, D., Verdoliva, L., Poggi, G.: Do gans leave artificial fingerprints? In: 2019 IEEE Conference on multimedia information processing and retrieval (MIPR), pp. 506\u2013511 (2019)","DOI":"10.1109\/MIPR.2019.00103"},{"key":"2981_CR84","doi-asserted-by":"crossref","unstructured":"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 (CVPR) workshops (2020)","DOI":"10.1109\/CVPRW50498.2020.00341"},{"key":"2981_CR85","unstructured":"Asnani, V., Yin, X., Hassner, T., Liu, X.: Reverse engineering of generative models: inferring model hyperparameters from generated images (2021) arXiv:2106.07873 [cs.CV]"},{"issue":"2","key":"2981_CR86","doi-asserted-by":"publisher","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":"2981_CR87","unstructured":"Albright, M., McCloskey, S.: Source generator attribution via inversion. In: The IEEE Conference on computer vision and pattern recognition (CVPR) Workshops (2019)"},{"key":"2981_CR88","doi-asserted-by":"crossref","unstructured":"Marra, F., Saltori, C., Boato, G., Verdoliva, L.: Incremental learning for the detection and classification of gan-generated images. In: 2019 IEEE International workshop on information forensics and security (WIFS), pp. 1\u20136 (2019)","DOI":"10.1109\/WIFS47025.2019.9035099"},{"key":"2981_CR89","unstructured":"Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. CoRR abs\/1802.05957 (2018) arXiv:1802.05957"},{"key":"2981_CR90","unstructured":"Bellemare, M.G., Danihelka, I., Dabney, W., Mohamed, S., Lakshminarayanan, B., Hoyer, S., Munos, R.: The cramer distance as a solution to biased wasserstein gradients. CoRR abs\/1705.10743 (2017) arXiv:1705.10743"},{"key":"2981_CR91","unstructured":"Bi\u0144kowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying MMD GANs. In: International conference on learning representations (2018). https:\/\/openreview.net\/forum?id=r1lUOzWCW"},{"key":"2981_CR92","unstructured":"100,000 Faces Generated by AI, 2018. https:\/\/generated.photos. Accessed: 2020-05-27"},{"issue":"5","key":"2981_CR93","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1109\/JSTSP.2020.3007250","volume":"14","author":"JC Neves","year":"2020","unstructured":"Neves, J.C., Tolosana, R., Vera-Rodriguez, R., Lopes, V.: Ganprintr: Improved fakes and evaluation of the state of the art in face manipulation detection. IEEE J Sel Top Signal Proc 14(5), 1038\u20131048 (2020)","journal-title":"IEEE J Sel Top Signal Proc"},{"key":"2981_CR94","unstructured":"Dufour, N., Gully, A., Karlsson, P., Vorbyov, A., Leung, T., Childs, J., Bregler, C.: Deepfakes detection dataset (2019) https:\/\/ai.googleblog.com\/2019\/09\/contributing-data-to-deepfake-detection.html"},{"key":"2981_CR95","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/978-3-642-01793-3_21","volume-title":"Advances in biometrics","author":"C Sanderson","year":"2009","unstructured":"Sanderson, C., Lovell, B.C.: Multi-region probabilistic histograms for robust and scalable identity inference. In: Tistarelli, M., Nixon, M.S. (eds.) Advances in biometrics, pp. 199\u2013208. Springer, Berlin (2009)"},{"key":"2981_CR96","unstructured":"FaceSwap-GAN. https:\/\/github.com\/shaoanlu\/faceswap-GAN. Accessed: 2020-03-16"},{"key":"2981_CR97","doi-asserted-by":"publisher","unstructured":"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: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 2886\u20132895 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00296","DOI":"10.1109\/CVPR42600.2020.00296"},{"key":"2981_CR98","unstructured":"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)"},{"key":"2981_CR99","unstructured":"Dolhansky, B., Howes, R., Pflaum, B., Baram, N., Ferrer, C.C.: The deepfake detection challenge (dfdc) preview dataset (2019) arXiv:1910.08854 [cs.CV]"},{"key":"2981_CR100","unstructured":"Dolhansky, B., Bitton, J., Pflaum, B., Lu, J., Howes, R., Wang, M., Ferrer, C.C.: The deepfake detection challenge (dfdc) dataset (2020) arXiv:2006.07397 [cs.CV]"},{"key":"2981_CR101","doi-asserted-by":"publisher","unstructured":"Korshunova, I., Shi, W., Dambre, J., Theis, L.: Fast face-swap using convolutional neural networks. In: 2017 IEEE International Conference on computer vision (ICCV), pp. 3697\u20133705 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.397","DOI":"10.1109\/ICCV.2017.397"},{"key":"2981_CR102","unstructured":"Perov, I., Gao, D., Chervoniy, N., Liu, K., Marangonda, S., Um\u00e9, C., Dpfks, M., Facenheim, C.S., RP, L., Jiang, J., Zhang, S., Wu, P., Zhou, B., Zhang, W.: Deepfacelab: Integrated, flexible and extensible face-swapping framework (2021) arXiv:2005.05535 [cs.CV]"},{"key":"2981_CR103","doi-asserted-by":"publisher","unstructured":"Nirkin, Y., Keller, Y., Hassner, T.: Fsgan: Subject agnostic face swapping and reenactment. In: 2019 IEEE\/CVF International Conference on computer vision (ICCV), pp. 7183\u20137192 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00728","DOI":"10.1109\/ICCV.2019.00728"},{"key":"2981_CR104","unstructured":"Jia, Y., Zhang, Y., Weiss, R.J., Wang, Q., Shen, J., Ren, F., Chen, Z., Nguyen, P., Pang, R., Moreno, I.L., Wu, Y.: Transfer learning from speaker verification to multispeaker text-to-speech synthesis (2019) arXiv:1806.04558 [cs.CL]"},{"key":"2981_CR105","doi-asserted-by":"crossref","unstructured":"Pu, J., Mangaokar, N., Kelly, L., Bhattacharya, P., Sundaram, K., Javed, M., Wang, B., Viswanath, B.: Deepfake videos in the wild: analysis and detection (2021) arXiv:2103.04263 [cs.CR]","DOI":"10.1145\/3442381.3449978"},{"key":"2981_CR106","doi-asserted-by":"crossref","unstructured":"Baltrusaitis, T., Zadeh, A., Lim, Y.C., Morency, L.-P.: Openface 2.0: Facial behavior analysis toolkit. In: 2018 13th IEEE International Conference on automatic face & gesture recognition (FG 2018), pp. 59\u201366 (2018). IEEE","DOI":"10.1109\/FG.2018.00019"},{"key":"2981_CR107","unstructured":"Fortune, S.: Handbook of discrete and computational geometry, pp. 377\u2013388. CRC Press, Inc., Boca Raton, FL, USA (1997). Chap. Voronoi Diagrams and Delaunay Triangulations. http:\/\/dl.acm.org\/citation.cfm?id=285869.285891"},{"issue":"12","key":"2981_CR108","doi-asserted-by":"publisher","first-page":"2781","DOI":"10.1109\/TBME.2017.2676160","volume":"64","author":"W Wang","year":"2017","unstructured":"Wang, W., Stuijk, S., de Haan, G.: Living-skin classification via remote-ppg. IEEE Trans Biomed Eng 64(12), 2781\u20132792 (2017)","journal-title":"IEEE Trans Biomed Eng"},{"key":"2981_CR109","doi-asserted-by":"crossref","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations (2015)","DOI":"10.1109\/ICCV.2015.314"},{"key":"2981_CR110","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: The IEEE conference on computer vision and pattern recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"2981_CR111","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: The IEEE conference on computer vision and pattern recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"2981_CR112","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE conference on computer vision and pattern recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2981_CR113","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. CoRR (2016) arXiv:1608.06993","DOI":"10.1109\/CVPR.2017.243"},{"key":"2981_CR114","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., et al.: An image is worth 16x16 words: Transformers for image recognition at scale (2020). arXiv:2010.11929"},{"key":"2981_CR115","unstructured":"vit-pytorch. https:\/\/github.com\/lucidrains\/vit-pytorch. Accessed 12 Nov 2022"},{"key":"2981_CR116","doi-asserted-by":"crossref","unstructured":"Mutegeki, R., Han, D.S.: A cnn-lstm approach to human activity recognition. In: 2020 international conference on artificial intelligence in information and communication (ICAIIC), IEEE, pp. 362\u2013366 (2020)","DOI":"10.1109\/ICAIIC48513.2020.9065078"},{"key":"2981_CR117","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6848\u20136856 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"2981_CR118","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR (2017) arXiv:1704.04861"},{"issue":"11","key":"2981_CR119","first-page":"120","volume":"25","author":"G Bradski","year":"2000","unstructured":"Bradski, G.: The openCV library. Dr. Dobbs J Softw Tools Prof Prog 25(11), 120\u2013123 (2000)","journal-title":"Dr. Dobbs J Softw Tools Prof Prog"},{"key":"2981_CR120","unstructured":"Chollet, F., et al.: Keras. https:\/\/keras.io (2015)"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02981-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-02981-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02981-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T21:19:26Z","timestamp":1730323166000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-02981-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,14]]},"references-count":120,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["2981"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-02981-0","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,14]]},"assertion":[{"value":"8 May 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This work is supported in part by the National Science Foundation under grant CNS-1629898. Dr. Demir receives a salary from IntelCorporation; Drs. Ciftci and Yin receive a salary from Binghamton University.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}