{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T07:23:46Z","timestamp":1787901826910,"version":"build-2784847793"},"reference-count":139,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00521-023-09288-0","type":"journal-article","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T09:02:15Z","timestamp":1702544535000},"page":"3859-3887","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A comprehensive evaluation of feature-based AI techniques for deepfake detection"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5741-3541","authenticated-orcid":false,"given":"Neha","family":"Sandotra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bhavna","family":"Arora","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"9288_CR1","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1145\/3072959.3073640","volume":"36","author":"S Suwajanakorn","year":"2017","unstructured":"Suwajanakorn S, Seitz SM, Kemelmacher-Shlizerman I (2017) Synthesizing Obama. ACM Trans Gr (TOG) 36:4. https:\/\/doi.org\/10.1145\/3072959.3073640","journal-title":"ACM Trans Gr (TOG)"},{"issue":"4","key":"9288_CR2","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1007\/s13347-021-00459-2","volume":"34","author":"A de Ruiter","year":"2021","unstructured":"de Ruiter A (2021) The distinct wrong of deepfakes. Philos Technol 34(4):1311\u20131332. https:\/\/doi.org\/10.1007\/s13347-021-00459-2","journal-title":"Philos Technol"},{"key":"9288_CR3","unstructured":"Ajder H, Patrini G, Cavalli F, and Cullen L, \u201cGraphic design: Eleanor Winter Cover image: Joel Filipe Cite: The State of Deepfakes: Landscape, Threats, and Impact About Deeptrace,\u201d 2019."},{"key":"9288_CR4","doi-asserted-by":"publisher","unstructured":"Guera D and Delp EJ (2019) \u201cDeepfake Video Detection Using Recurrent Neural Networks,\u201d Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Feb. https:\/\/doi.org\/10.1109\/AVSS.2018.8639163.","DOI":"10.1109\/AVSS.2018.8639163"},{"key":"9288_CR5","doi-asserted-by":"publisher","unstructured":"Kim T, Kim J, Kim J and Woo SS (2022) \u201cA Face Pre-Processing Approach to Evade Deepfake Detector,\u201d WDC 2022 - Proceedings of the 1st Workshop on Security Implications of Deepfakes and Cheapfakes, pp. 35\u201338,  https:\/\/doi.org\/10.1145\/3494109.3527190.","DOI":"10.1145\/3494109.3527190"},{"key":"9288_CR6","unstructured":"\u201cRandom Face Generator- This Person Does Not Exist.\u201d https:\/\/this-person-does-not-exist.com\/en (accessed Sep. 07, 2022)."},{"key":"9288_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03766-z","author":"M Masood","year":"2022","unstructured":"Masood M, Nawaz M, Malik KM, Javed A, Irtaza A, Malik H (2022) Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-022-03766-z","journal-title":"Appl Intell"},{"key":"9288_CR8","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.future.2021.06.043","volume":"125","author":"J Yang","year":"2021","unstructured":"Yang J, Xiao S, Li A, Lan G, Wang H (2021) Detecting fake images by identifying potential texture difference. Futur Gener Comput Syst 125:127\u2013135. https:\/\/doi.org\/10.1016\/j.future.2021.06.043","journal-title":"Futur Gener Comput Syst"},{"issue":"1","key":"9288_CR9","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA (2018) Generative adversarial networks: an overview. IEEE Signal Process Mag 35(1):53\u201365. https:\/\/doi.org\/10.1109\/MSP.2017.2765202","journal-title":"IEEE Signal Process Mag"},{"key":"9288_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/J.CVIU.2022.103525","volume":"223","author":"TT Nguyen","year":"2022","unstructured":"Nguyen TT et al (2022) Deep learning for deepfakes creation and detection: A survey. Comput Vis Image Underst 223:103525. https:\/\/doi.org\/10.1016\/J.CVIU.2022.103525","journal-title":"Comput Vis Image Underst"},{"key":"9288_CR11","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 (2020) Deepfakes and beyond: a Survey of face manipulation and fake detection. Information Fusion 64:131\u2013148. https:\/\/doi.org\/10.1016\/j.inffus.2020.06.014","journal-title":"Information Fusion"},{"key":"9288_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jjimei.2021.100054","volume":"2","author":"S Kolagati","year":"2022","unstructured":"Kolagati S, Priyadharshini T, Rajam VMA (2022) Exposing deepfakes using a deep multilayer perceptron \u2013 convolutional neural network model. Int J Inf Manag Data Insights 2:1. https:\/\/doi.org\/10.1016\/j.jjimei.2021.100054","journal-title":"Int J Inf Manag Data Insights"},{"key":"9288_CR13","doi-asserted-by":"publisher","unstructured":"Afchar D, Nozick V, Yamagishi J and Echizen I (2019) \u201cMesoNet: A compact facial video forgery detection network,\u201d 10th IEEE International Workshop on Information Forensics and Security, WIFS 2018, Jan.  https:\/\/doi.org\/10.1109\/WIFS.2018.8630761.","DOI":"10.1109\/WIFS.2018.8630761"},{"key":"9288_CR14","doi-asserted-by":"publisher","unstructured":"Mehta V, Gupta P, Subramanian R, and Dhall A (2021) \u201cFakeBuster: A deepfakes detection tool for video conferencing scenarios,\u201d International Conference on Intelligent User Interfaces, Proceedings IUI, pp. 61\u201363, Apr. https:\/\/doi.org\/10.1145\/3397482.3450726.","DOI":"10.1145\/3397482.3450726"},{"issue":"6","key":"9288_CR15","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1049\/BME2.12031","volume":"10","author":"P Yu","year":"2021","unstructured":"Yu P, Xia Z, Fei J, Lu Y (2021) A survey on deepfake video detection. IET Biom 10(6):607\u2013624. https:\/\/doi.org\/10.1049\/BME2.12031","journal-title":"IET Biom"},{"key":"9288_CR16","doi-asserted-by":"crossref","unstructured":"Chadha A, Kumar V, Kashyap S and Gupta M (2021) \u201cDeepfake: An Overview,\u201d in Lecture Notes in Networks and Systems, Springer Science and Business Media Deutschland GmbH, pp 557\u2013566.","DOI":"10.1007\/978-981-16-0733-2_39"},{"key":"9288_CR17","doi-asserted-by":"publisher","first-page":"18757","DOI":"10.1109\/ACCESS.2022.3151186","volume":"10","author":"A Malik","year":"2022","unstructured":"Malik A, Kuribayashi M, Abdullahi SM, Khan AN (2022) DeepFake detection for human face images and videos: a survey. IEEE Access 10:18757\u201318775. https:\/\/doi.org\/10.1109\/ACCESS.2022.3151186","journal-title":"IEEE Access"},{"key":"9288_CR18","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/J.NEUCOM.2022.09.135","volume":"513","author":"JW Seow","year":"2022","unstructured":"Seow JW, Lim MK, Phan RCW, Liu JK (2022) A comprehensive overview of Deepfake: Generation, detection, datasets, and opportunities. Neurocomputing 513:351\u2013371. https:\/\/doi.org\/10.1016\/J.NEUCOM.2022.09.135","journal-title":"Neurocomputing"},{"key":"9288_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2022.103525","author":"TT Nguyen","year":"2022","unstructured":"Nguyen TT et al (2022) Deep learning for deepfakes creation and detection: a survey. Comput Vision and Image Understanding. https:\/\/doi.org\/10.1016\/j.cviu.2022.103525","journal-title":"Comput Vision and Image Understanding"},{"issue":"21","key":"9288_CR20","doi-asserted-by":"publisher","first-page":"15273","DOI":"10.1007\/S00521-023-08480-6\/TABLES\/16","volume":"35","author":"S Al-Janabi","year":"2023","unstructured":"Al-Janabi S, Al-Janabi Z (2023) Development of deep learning method for predicting DC power based on renewable solar energy and multi-parameters function. Neural Comput Appl 35(21):15273\u201315294. https:\/\/doi.org\/10.1007\/S00521-023-08480-6\/TABLES\/16","journal-title":"Neural Comput Appl"},{"issue":"14","key":"9288_CR21","doi-asserted-by":"publisher","first-page":"10943","DOI":"10.1007\/S00500-020-04905-9\/TABLES\/7","volume":"24","author":"S Al-Janabi","year":"2020","unstructured":"Al-Janabi S, Alkaim AF, Adel Z (2020) An Innovative synthesis of deep learning techniques (DCapsNet & DCOM) for generation electrical renewable energy from wind energy. Soft comput 24(14):10943\u201310962. https:\/\/doi.org\/10.1007\/S00500-020-04905-9\/TABLES\/7","journal-title":"Soft comput"},{"issue":"7","key":"9288_CR22","doi-asserted-by":"publisher","first-page":"4081","DOI":"10.1007\/S11831-023-09927-8\/FIGURES\/6","volume":"30","author":"K Singh","year":"2023","unstructured":"Singh K, Malhotra D (2023) Meta-health: learning-to-learn (meta-learning) as a next generation of deep learning exploring healthcare challenges and solutions for rare disorders: a systematic analysis. Arch Comput Methods Eng 30(7):4081\u20134112. https:\/\/doi.org\/10.1007\/S11831-023-09927-8\/FIGURES\/6","journal-title":"Arch Comput Methods Eng"},{"issue":"2","key":"9288_CR23","doi-asserted-by":"publisher","first-page":"115","DOI":"10.11591\/ijict.v12i2.pp115-126","volume":"12","author":"N Sandotra","year":"2023","unstructured":"Sandotra N, Mahajan P, Abrol P, Lehana PK (2023) Analyzing performance of deep learning models under the presence of distortions in identifying plant leaf disease. Int J Inf Commun Technol IJ-ICT 12(2):115. https:\/\/doi.org\/10.11591\/ijict.v12i2.pp115-126","journal-title":"Int J Inf Commun Technol IJ-ICT"},{"key":"9288_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1063\/5.0108884\/2829443","volume":"2555","author":"S Gupta","year":"2022","unstructured":"Gupta S, Sharma P (2022) Machine learning approach for heart disease prediction: a survey. AIP Conf Proc 2555:1. https:\/\/doi.org\/10.1063\/5.0108884\/2829443","journal-title":"AIP Conf Proc"},{"key":"9288_CR25","doi-asserted-by":"publisher","unstructured":"Bregler C, Covell M and Slaney M (1997) \u201cVideo Rewrite: Driving visual speech with audio,\u201d Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1997, pp 353\u2013360, ,  https:\/\/doi.org\/10.1145\/258734.258880.","DOI":"10.1145\/258734.258880"},{"key":"9288_CR26","unstructured":"\u201cDARPA is funding new tech that can identify manipulated videos and \u2018deepfakes\u2019 | TechCrunch.\u201d https:\/\/techcrunch.com\/2018\/04\/30\/deepfakes-fake-videos-darpa-sri-international-media-forensics\/ (accessed Sep. 29, 2022)."},{"key":"9288_CR27","doi-asserted-by":"publisher","DOI":"10.1145\/3425780","author":"Y Mirsky","year":"2020","unstructured":"Mirsky Y, Lee W (2020) The creation and detection of deepfakes: a survey. ACM Comput Surv. https:\/\/doi.org\/10.1145\/3425780","journal-title":"ACM Comput Surv"},{"key":"9288_CR28","doi-asserted-by":"publisher","unstructured":"Chan C, Ginosar S, Zhou T, and Efros A (2018) \u201cEverybody Dance Now,\u201d Proceedings of the IEEE International Conference on Computer Vision, vol. 2019-October, pp. 5932\u20135941,  https:\/\/doi.org\/10.48550\/arxiv.1808.07371.","DOI":"10.48550\/arxiv.1808.07371"},{"key":"9288_CR29","doi-asserted-by":"publisher","unstructured":"Sharma M and Kaur M (2022) \u201cA Review of deepfake technology: an emerging ai threat,\u201d Soft Computing for Security Applications, Proceedings of ICSCS, pp. 605\u2013619,  https:\/\/doi.org\/10.1007\/978-981-16-5301-8_44.","DOI":"10.1007\/978-981-16-5301-8_44"},{"key":"9288_CR30","doi-asserted-by":"publisher","first-page":"25494","DOI":"10.1109\/ACCESS.2022.3154404","volume":"10","author":"MS Rana","year":"2022","unstructured":"Rana MS, Nobi MN, Murali B, Sung AH (2022) Deepfake detection: a systematic literature review. IEEE Access 10:25494\u201325513. https:\/\/doi.org\/10.1109\/ACCESS.2022.3154404","journal-title":"IEEE Access"},{"key":"9288_CR31","doi-asserted-by":"publisher","unstructured":"Dolhansky B et al., \u201cThe DeepFake Detection Challenge (DFDC) Dataset,\u201d Computer Vision and Pattern Recognition, vol. 1, Jun. 2020,  https:\/\/doi.org\/10.48550\/arxiv.2006.07397.","DOI":"10.48550\/arxiv.2006.07397"},{"key":"9288_CR32","unstructured":"Li Y, Yang X, Sun P, Qi H and Lyu S \u201cCeleb-DF: A Large-scale Challenging Dataset for DeepFake Forensics.\u201d [Online]. Available: https:\/\/deepfakedetectionchallenge.ai."},{"key":"9288_CR33","doi-asserted-by":"publisher","unstructured":"Mehta V, Gupta P, Subramanian R and Dhall A 2021 \u201cFakeBuster: A DeepFakes Detection Tool for Video Conferencing Scenarios,\u201d International Conference on Intelligent User Interfaces, Proceedings IUI, pp. 61\u201363, https:\/\/doi.org\/10.48550\/arxiv.2101.03321.","DOI":"10.48550\/arxiv.2101.03321"},{"key":"9288_CR34","doi-asserted-by":"publisher","DOI":"10.1145\/3550454.3555436","author":"Y Nitzan","year":"2022","unstructured":"Nitzan Y et al (2022) MyStyle: a personalized generative prior. ACM Trans Gr (TOG). https:\/\/doi.org\/10.1145\/3550454.3555436","journal-title":"ACM Trans Gr (TOG)"},{"key":"9288_CR35","doi-asserted-by":"publisher","unstructured":"Wang Y, Bilinski P, Bremond F and Dantcheva A (2020) \u201cImaGINator: Conditional spatio-temporal GAN for video generation,\u201d In: Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, pp. 1149\u20131158,  https:\/\/doi.org\/10.1109\/WACV45572.2020.9093492.","DOI":"10.1109\/WACV45572.2020.9093492"},{"key":"9288_CR36","unstructured":"\u201cDeepFake Video: Cadbury\u2019s New AI Tool Allows to Create Free Ads Featuring Shah Rukh Khan\u2019s Face and Voice.\u201d https:\/\/www.thehansindia.com\/technology\/tech-news\/cadburys-new-ai-tool-allows-to-create-free-ads-featuring-shah-rukh-khans-face-and-voice-712400 (accessed Sep. 15, 2022)."},{"key":"9288_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/J.ESWA.2021.115465","volume":"184","author":"DM Ballesteros","year":"2021","unstructured":"Ballesteros DM, Rodriguez-Ortega Y, Renza D, Arce G (2021) Deep4SNet: deep learning for fake speech classification. Expert Syst Appl 184:115465. https:\/\/doi.org\/10.1016\/J.ESWA.2021.115465","journal-title":"Expert Syst Appl"},{"key":"9288_CR38","unstructured":"\u201cFraudsters Used AI to Mimic CEO\u2019s Voice in Unusual Cybercrime Case - WSJ.\u201d https:\/\/www.wsj.com\/articles\/fraudsters-use-ai-to-mimic-ceos-voice-in-unusual-cybercrime-case-11567157402 (accessed Sep. 15, 2022)."},{"key":"9288_CR39","doi-asserted-by":"publisher","unstructured":"Todisco M et al., (2019) \u201cASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection,\u201d In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, vol. 2019-September, pp. 1008\u20131012,  https:\/\/doi.org\/10.48550\/arxiv.1904.05441.","DOI":"10.48550\/arxiv.1904.05441"},{"key":"9288_CR40","unstructured":"\u201cGitHub - iperov\/DeepFaceLive: Real-time face swap for PC streaming or video calls.\u201d https:\/\/github.com\/iperov\/DeepFaceLive (accessed Mar. 29, 2023)."},{"key":"9288_CR41","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 (2015) Real-time expression transfer for facial reenactment. ACM Trans Gr (TOG). https:\/\/doi.org\/10.1145\/2816795.2818056","journal-title":"ACM Trans Gr (TOG)"},{"issue":"1","key":"9288_CR42","doi-asserted-by":"publisher","first-page":"96","DOI":"10.48550\/arxiv.2007.14808","volume":"62","author":"J Thies","year":"2020","unstructured":"Thies J, Zollh\u00f6fer M, Stamminger M, Theobalt C, Nie\u00dfner M (2020) Face2Face: real-time face capture and reenactment of RGB videos. Commun ACM 62(1):96\u2013104. https:\/\/doi.org\/10.48550\/arxiv.2007.14808","journal-title":"Commun ACM"},{"key":"9288_CR43","doi-asserted-by":"publisher","DOI":"10.1145\/2766943","author":"C Cao","year":"2015","unstructured":"Cao C, Bradley D, Zhou K, Beeler T (2015) Real-time high-fidelity facial performance capture. ACM Transactions on Graphics (TOG). https:\/\/doi.org\/10.1145\/2766943","journal-title":"ACM Transactions on Graphics (TOG)"},{"issue":"3","key":"9288_CR44","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s13735-022-00241-w","volume":"11","author":"D Dagar","year":"2022","unstructured":"Dagar D, Vishwakarma DK (2022) A literature review and perspectives in deepfakes: generation, detection, and applications. Int J Multimed Inf Retr 11(3):219\u2013289. https:\/\/doi.org\/10.1007\/s13735-022-00241-w","journal-title":"Int J Multimed Inf Retr"},{"key":"9288_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/J.ESWA.2021.115742","volume":"186","author":"S Rao","year":"2021","unstructured":"Rao S, Verma AK, Bhatia T (2021) A review on social spam detection: Challenges, open issues, and future directions. Expert Syst Appl 186:115742. https:\/\/doi.org\/10.1016\/J.ESWA.2021.115742","journal-title":"Expert Syst Appl"},{"issue":"3","key":"9288_CR46","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1109\/TAI.2021.3064901","volume":"1","author":"PK Roy","year":"2020","unstructured":"Roy PK, Chahar S (2020) Fake profile detection on social networking websites: a comprehensive review. IEEE Trans Artificial Intell 1(3):271\u2013285. https:\/\/doi.org\/10.1109\/TAI.2021.3064901","journal-title":"IEEE Trans Artificial Intell"},{"key":"9288_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01606-8","author":"F Juefei-Xu","year":"2022","unstructured":"Juefei-Xu F, Wang R, Huang Y, Guo Q, Ma L, Liu Y (2022) Countering malicious deepfakes: survey, battleground, and horizon. Int J Comput Vis. https:\/\/doi.org\/10.1007\/s11263-022-01606-8","journal-title":"Int J Comput Vis"},{"key":"9288_CR48","doi-asserted-by":"publisher","DOI":"10.2139\/SSRN.3213954","author":"R Chesney","year":"2018","unstructured":"Chesney R, Citron DK (2018) Deep fakes: a looming challenge for privacy, democracy, and national security. SSRN Electron J. https:\/\/doi.org\/10.2139\/SSRN.3213954","journal-title":"SSRN Electron J"},{"key":"9288_CR49","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/978-981-19-2130-8_28\/COVER","volume":"461","author":"R Rani","year":"2022","unstructured":"Rani R, Kumar T, Sah MP (2022) A review on deepfake media detection. Lecture Notes in Netw Syst 461:343\u2013356. https:\/\/doi.org\/10.1007\/978-981-19-2130-8_28\/COVER","journal-title":"Lecture Notes in Netw Syst"},{"key":"9288_CR50","unstructured":"Korshunov P and Marcel S, \u201cDeepFakes: a New Threat to Face Recognition? Assessment and Detection,\u201d Dec. 2018, [Online]. Available: http:\/\/arxiv.org\/abs\/1812.08685"},{"key":"9288_CR51","doi-asserted-by":"publisher","unstructured":"Yang X, Li Y, and Lyu S (2019) \u201cExposing deep fakes using inconsistent head poses,\u201d ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing In: Proceedings, vol. 2019-May, pp. 8261\u20138265,  https:\/\/doi.org\/10.1109\/ICASSP.2019.8683164.","DOI":"10.1109\/ICASSP.2019.8683164"},{"key":"9288_CR52","doi-asserted-by":"publisher","DOI":"10.17632\/H4YMVY9G8J.1","author":"S Lee","year":"2020","unstructured":"Lee S, Tariq S, Shin Y (2020) \u201cHand-crafted Facial Manipulation (HFM) Dataset. Mendeley Data. https:\/\/doi.org\/10.17632\/H4YMVY9G8J.1","journal-title":"Mendeley Data"},{"key":"9288_CR53","doi-asserted-by":"crossref","unstructured":"R\u00f6ssler A, Cozzolino D, Verdoliva L, Riess C, Thies J, and Nie\u00dfner M (2019) \u201cFaceForensics++: Learning to Detect Manipulated Facial Images,\u201d [Online]. Available: http:\/\/arxiv.org\/abs\/1901.08971","DOI":"10.1109\/ICCV.2019.00009"},{"key":"9288_CR54","doi-asserted-by":"publisher","unstructured":"Li Y, Yang X, Sun P, Qi H, and Lyu S, (2020) \u201cCeleb-DF: a large-scale challenging dataset for deepfake forensics,\u201dIn: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3204\u20133213,  https:\/\/doi.org\/10.1109\/CVPR42600.2020.00327.","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"9288_CR55","unstructured":"Huang J, Wang X, Du B, Du P, and Xu AC \u201cDeepFake MNIST+: A DeepFake Facial Animation Dataset.\u201d [Online]. Available: https:\/\/github.com\/huangjiadidi\/DeepFakeMnist"},{"key":"9288_CR56","doi-asserted-by":"publisher","unstructured":"Zi B, Chang M, Chen J, Ma X, and Jiang YG (2020) \u201cWildDeepfake: A Challenging Real-World Dataset for Deepfake Detection,\u201d in MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia, Association for Computing Machinery, Inc, Oct. pp. 2382\u20132390.  https:\/\/doi.org\/10.1145\/3394171.3413769.","DOI":"10.1145\/3394171.3413769"},{"key":"9288_CR57","unstructured":"\u201c140k Real and Fake Faces | Kaggle.\u201d https:\/\/www.kaggle.com\/datasets\/xhlulu\/140k-real-and-fake-faces (accessed Mar. 21, 2023)."},{"key":"9288_CR58","doi-asserted-by":"publisher","unstructured":"He Y et al. (2021) \u201cForgeryNet: A versatile benchmark for comprehensive forgery analysis,\u201d 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4358\u20134367,  https:\/\/doi.org\/10.1109\/CVPR46437.2021.00434.","DOI":"10.1109\/CVPR46437.2021.00434"},{"key":"9288_CR59","doi-asserted-by":"crossref","unstructured":"Dang H, Liu F, Stehouwer J, Liu X and Jain A (2019) \u201cOn the detection of digital face manipulation,\u201d [Online]. Available: http:\/\/arxiv.org\/abs\/1910.01717","DOI":"10.1109\/CVPR42600.2020.00582"},{"key":"9288_CR60","doi-asserted-by":"publisher","unstructured":"Jain A, Korshunov P, and Marcel S (2021) \u201cImproving generalization of deepfake detection by training for attribution,\u201d IEEE 23rd International Workshop on Multimedia Signal Processing, MMSP 2021,  https:\/\/doi.org\/10.1109\/MMSP53017.2021.9733468.","DOI":"10.1109\/MMSP53017.2021.9733468"},{"key":"9288_CR61","unstructured":"\u201cFakeApp 2.2.0.\u201d  (2022) https:\/\/www.malavida.com\/en\/soft\/fakeapp\/#gref (accessed Sep. 12)."},{"key":"9288_CR62","unstructured":"Faceswap (2022), \u201cDeepfakes software for all\u201d https:\/\/github.com\/topics\/faceswap (accessed Sep. 12)."},{"key":"9288_CR63","doi-asserted-by":"publisher","first-page":"2153","DOI":"10.1016\/j.procs.2023.01.191","volume":"218","author":"MK Preeti","year":"2023","unstructured":"Preeti MK, Sharma HK (2023) A GAN-based model of deepfake detection in social media. Procedia Comput Sci 218:2153\u20132162. https:\/\/doi.org\/10.1016\/j.procs.2023.01.191","journal-title":"Procedia Comput Sci"},{"key":"9288_CR64","unstructured":"Shen T et al., (2023) \u201c\u2018deep fakes\u2019 using generative adversarial networks (gan),\u201d noiselab.ucsd.edu, 2018, Accessed: Mar. 21[Online]. Available: http:\/\/noiselab.ucsd.edu\/ECE228_2018\/Reports\/Report16.pdf"},{"key":"9288_CR65","doi-asserted-by":"publisher","unstructured":"Brock A, Donahue J, and Simonyan K (2018) \u201cLarge scale GAN training for high fidelity natural image synthesis,\u201d 7th International Conference on Learning Representations, ICLR 2019, Sep. https:\/\/doi.org\/10.48550\/arxiv.1809.11096.","DOI":"10.48550\/arxiv.1809.11096"},{"key":"9288_CR66","doi-asserted-by":"publisher","unstructured":"Li L, Bao J, Yang H, Chen D, and Wen F (2020) \u201cAdvancing high fidelity identity swapping for forgery detection,\u201d Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 5073\u20135082, https:\/\/doi.org\/10.1109\/CVPR42600.2020.00512.","DOI":"10.1109\/CVPR42600.2020.00512"},{"key":"9288_CR67","doi-asserted-by":"publisher","unstructured":"Korshunova I, Shi W, Dambre J, and Theis L, (2017) \u201cFast face-swap using convolutional neural networks,\u201d 2017 IEEE International Conference on Computer Vision (ICCV), vol. 2017-October, pp. 3697\u20133705, https:\/\/doi.org\/10.1109\/ICCV.2017.397.","DOI":"10.1109\/ICCV.2017.397"},{"key":"9288_CR68","doi-asserted-by":"publisher","unstructured":"Natsume R, Yatagawa T, and Morishima S, \u201cFSNet: An Identity-Aware Generative Model for Image-Based Face Swapping,\u201d Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11366 LNCS, pp. 117\u2013132, 2019, https:\/\/doi.org\/10.1007\/978-3-030-20876-9_8\/COVER.","DOI":"10.1007\/978-3-030-20876-9_8\/COVER"},{"key":"9288_CR69","doi-asserted-by":"publisher","unstructured":"Prajwal KR, Jha A, Mukhopadhyay R, Namboodiri V, Philip J and Jawahar Cv , (2019) \u201cTowards automatic face-to-face translation,\u201d in MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia, Association for Computing Machinery, Inc, pp. 1428\u20131436. https:\/\/doi.org\/10.1145\/3343031.3351066.","DOI":"10.1145\/3343031.3351066"},{"key":"9288_CR70","doi-asserted-by":"publisher","unstructured":"Prajwal KR, Mukhopadhyay R, Namboodiri VP, and Jawahar Cv (2020) \u201cA lip sync expert is all you need for speech to lip generation in the wild,\u201d in MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia, Association for Computing Machinery, Inc, pp. 484\u2013492. https:\/\/doi.org\/10.1145\/3394171.3413532.","DOI":"10.1145\/3394171.3413532"},{"key":"9288_CR71","doi-asserted-by":"crossref","unstructured":"Nirkin Y, Keller Y, and Hassner T (2019) \u201cFSGAN: Subject Agnostic Face Swapping and Reenactment,\u201d [Online]. Available: http:\/\/arxiv.org\/abs\/1908.05932","DOI":"10.1109\/ICCV.2019.00728"},{"key":"9288_CR72","unstructured":"Hao H, Baireddy S, Reibman AR, and Delp EJ (2020) \u201cFaR-GAN for One-Shot Face Reenactment,\u201d May [Online]. Available: http:\/\/arxiv.org\/abs\/2005.06402"},{"key":"9288_CR73","doi-asserted-by":"crossref","unstructured":"Pumarola A, Agudo A, Martinez AM, Sanfeliu A and Moreno-Noguer F (2018) \u201cGANimation: Anatomically-aware Facial Animation from a Single Image,\u201d[Online]. Available: http:\/\/arxiv.org\/abs\/1807.09251","DOI":"10.1007\/978-3-030-01249-6_50"},{"key":"9288_CR74","unstructured":"Zhang H, Goodfellow I, Metaxas D, and Odena A, (2018) \u201cSelf-Attention Generative Adversarial Networks,\u201d [Online]. Available: http:\/\/arxiv.org\/abs\/1805.08318"},{"key":"9288_CR75","doi-asserted-by":"crossref","unstructured":"Huang R, Zhang S, Li T, and He R, (2017) \u201cBeyond face rotation: global and local perception GAN for photorealistic and identity preserving frontal view synthesis,\u201d [Online]. Available: http:\/\/arxiv.org\/abs\/1704.04086","DOI":"10.1109\/ICCV.2017.267"},{"key":"9288_CR76","doi-asserted-by":"crossref","unstructured":"Choi Y, Uh Y, Yoo J, and Ha JW (2019) \u201cStarGAN v2: Diverse Image Synthesis for Multiple Domains,\u201d [Online]. Available: http:\/\/arxiv.org\/abs\/1912.01865","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"9288_CR77","unstructured":"He Z, Kan M, Zhang J, and Shan S (2020) \u201cPA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing,\u201d [Online]. Available: http:\/\/arxiv.org\/abs\/2007.05892"},{"key":"9288_CR78","doi-asserted-by":"publisher","unstructured":"Dale K, Sunkavalli K, Johnson MK, Vlasic D, Matusik W, and Pfister H (2011) \u201cVideo face replacement,\u201d Proceedings of the 2011 SIGGRAPH Asia Conference, vol. 30, no. 6, Dec. https:\/\/doi.org\/10.1145\/2024156.2024164.","DOI":"10.1145\/2024156.2024164"},{"key":"9288_CR79","doi-asserted-by":"publisher","unstructured":"Karras T, Aila T, Laine S, and Lehtinen J (2017) \u201cProgressive growing of GANs for improved quality, stability, and variation,\u201d 6th international conference on learning representations, ICLR 2018 - Conference Track Proceedings, https:\/\/doi.org\/10.48550\/arxiv.1710.10196.","DOI":"10.48550\/arxiv.1710.10196"},{"issue":"12","key":"9288_CR80","doi-asserted-by":"publisher","first-page":"4217","DOI":"10.48550\/arxiv.1812.04948","volume":"43","author":"T Karras","year":"2018","unstructured":"Karras T, Laine S, Aila T (2018) A style-based generator architecture for generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 43(12):4217\u20134228. https:\/\/doi.org\/10.48550\/arxiv.1812.04948","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9288_CR81","doi-asserted-by":"publisher","unstructured":"Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J and Aila T (2019) \u201cAnalyzing and Improving the Image Quality of StyleGAN,\u201d Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp. 8107\u20138116, https:\/\/doi.org\/10.48550\/arxiv.1912.04958.","DOI":"10.48550\/arxiv.1912.04958"},{"key":"9288_CR82","doi-asserted-by":"publisher","unstructured":"Thies J, Zollhofer M, Stamminger M, Theobalt C and Niebner M (2016) \u201cFace2Face: real-time face capture and reenactment of RGB videos,\u201d Proceedings of the IEEE computer society conference on computer vision and pattern recognition, vol. 2016-December, pp. 2387\u20132395, Dec. https:\/\/doi.org\/10.1109\/CVPR.2016.262.","DOI":"10.1109\/CVPR.2016.262"},{"key":"9288_CR83","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.1904.12356","author":"J Thies","year":"2019","unstructured":"Thies J, Zollh\u00f6fer M, Nie\u00dfner M (2019) Deferred neural rendering: image synthesis using neural textures. ACM Trans Graph. https:\/\/doi.org\/10.48550\/arxiv.1904.12356","journal-title":"ACM Trans Graph"},{"key":"9288_CR84","doi-asserted-by":"publisher","DOI":"10.1145\/3333002","author":"L Liu","year":"2019","unstructured":"Liu L et al (2019) Neural Rendering and Reenactment of Human Actor Videos. ACM Trans Gr (TOG). https:\/\/doi.org\/10.1145\/3333002","journal-title":"ACM Trans Gr (TOG)"},{"issue":"1","key":"9288_CR85","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/TBIOM.2021.3049576","volume":"3","author":"MC Doukas","year":"2021","unstructured":"Doukas MC, Koujan MR, Sharmanska V, Roussos A, Zafeiriou S (2021) Head2Head++: deep facial attributes re-targeting. IEEE Trans Biom Behav Identity Sci 3(1):31\u201343. https:\/\/doi.org\/10.1109\/TBIOM.2021.3049576","journal-title":"IEEE Trans Biom Behav Identity Sci"},{"key":"9288_CR86","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.1910.12713","author":"TC Wang","year":"2019","unstructured":"Wang TC, Liu MY, Tao A, Liu G, Kautz J, Catanzaro B (2019) Few-shot video-to-video synthesis. Adv Neural Inf Process Syst. https:\/\/doi.org\/10.48550\/arxiv.1910.12713","journal-title":"Adv Neural Inf Process Syst"},{"key":"9288_CR87","doi-asserted-by":"publisher","unstructured":"Gafni O, Ashual O, and Wolf L (2020) \u201cSingle-Shot Freestyle Dance Reenactment,\u201d Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 882\u2013891, https:\/\/doi.org\/10.48550\/arxiv.2012.01158.","DOI":"10.48550\/arxiv.2012.01158"},{"key":"9288_CR88","doi-asserted-by":"publisher","unstructured":"Zhang J et al., (2019) \u201cFReeNet: Multi-Identity Face Reenactment,\u201d Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 5325\u20135334, May https:\/\/doi.org\/10.48550\/arxiv.1905.11805.","DOI":"10.48550\/arxiv.1905.11805"},{"key":"9288_CR89","doi-asserted-by":"publisher","unstructured":"Zhang Y, Zhang S, He Y, Li C, Loy CC, and Liu Z (2019) \u201cOne-shot Face Reenactment,\u201d 30th British Machine Vision Conference 2019, BMVC 2019, https:\/\/doi.org\/10.48550\/arxiv.1908.03251.","DOI":"10.48550\/arxiv.1908.03251"},{"key":"9288_CR90","doi-asserted-by":"publisher","unstructured":"Gu K, Zhou Y, and Huang T (2019) \u201cFLNet: Landmark Driven Fetching and Learning Network for Faithful Talking Facial Animation Synthesis,\u201d AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, pp. 10861\u201310868, https:\/\/doi.org\/10.48550\/arxiv.1911.09224.","DOI":"10.48550\/arxiv.1911.09224"},{"key":"9288_CR91","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.1910.04742","author":"J Lee","year":"2019","unstructured":"Lee J, Ramanan D, Girdhar R (2019) MetaPix: few-shot video retargeting. Int Conf Learn Represent (ICLR).","journal-title":"Int Conf Learn Represent (ICLR)"},{"issue":"4","key":"9288_CR92","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1145\/3306346","volume":"38","author":"O Fried","year":"2019","unstructured":"Fried O et al (2019) Text-based Editing of Talking-head Video. ACM Trans Graph 38(4):14. https:\/\/doi.org\/10.1145\/3306346","journal-title":"ACM Trans Graph"},{"key":"9288_CR93","doi-asserted-by":"publisher","unstructured":"Lahiri A, Kwatra V, Frueh C, Lewis J, and Bregler C (2021) \u201cLipsyNc3D: Data-Efficient Learning of Personalized 3D Talking Faces from Video using Pose and Lighting Normalization,\u201d Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2754\u20132763, https:\/\/doi.org\/10.1109\/CVPR46437.2021.00278.","DOI":"10.1109\/CVPR46437.2021.00278"},{"issue":"11\u201312","key":"9288_CR94","doi-asserted-by":"publisher","first-page":"1767","DOI":"10.1007\/s11263-019-01150-y","volume":"127","author":"A Jamaludin","year":"2019","unstructured":"Jamaludin A, Chung JS, Zisserman A (2019) You said that?: synthesising talking faces from audio. Int J Comput Vis 127(11\u201312):1767\u20131779","journal-title":"Int J Comput Vis"},{"issue":"4","key":"9288_CR95","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3306346.3323028","volume":"38","author":"O Fried","year":"2019","unstructured":"Fried O et al (2019) Text-based Editing of Talking-head Video. ACM Trans Graph 38(4):1\u201314.","journal-title":"ACM Trans Graph"},{"key":"9288_CR96","doi-asserted-by":"publisher","unstructured":"Zhang Z, Li L, Ding Y, and Fan C (2021) \u201cFlow-guided One-shot Talking Face Generation with a High-resolution Audio-visual Dataset,\u201d Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3660\u20133669, https:\/\/doi.org\/10.1109\/CVPR46437.2021.00366.","DOI":"10.1109\/CVPR46437.2021.00366"},{"key":"9288_CR97","doi-asserted-by":"publisher","unstructured":"Ak K, Kassim A, Lim JH, and  Tham JY, (2019) \u201cAttribute manipulation generative adversarial networks for fashion images,\u201d Proceedings of the IEEE International Conference on Computer Vision, vol. 2019-October, pp. 10540\u201310549, https:\/\/doi.org\/10.1109\/ICCV.2019.01064.","DOI":"10.1109\/ICCV.2019.01064"},{"key":"9288_CR98","doi-asserted-by":"publisher","unstructured":"Choi Y, Choi M, Kim M, Ha JW, Kim S and Choo J (2017) \u201cStarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation,\u201d Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 8789\u20138797, https:\/\/doi.org\/10.48550\/arxiv.1711.09020.","DOI":"10.48550\/arxiv.1711.09020"},{"key":"9288_CR99","doi-asserted-by":"publisher","unstructured":"Pumarola A, Agudo A, Martinez AM, Sanfeliu A, and Moreno-Noguer F (2018) \u201cGANimation: Anatomically-aware Facial Animation from a Single Image,\u201d Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11214 LNCS, pp. 835\u2013851, https:\/\/doi.org\/10.48550\/arxiv.1807.09251.","DOI":"10.48550\/arxiv.1807.09251"},{"key":"9288_CR100","doi-asserted-by":"publisher","unstructured":"Liu M et al., (2019) \u201cSTGAN: A unified selective transfer network for arbitrary image attribute editing,\u201d Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 3668\u20133677, https:\/\/doi.org\/10.48550\/arxiv.1904.09709.","DOI":"10.48550\/arxiv.1904.09709"},{"key":"9288_CR101","doi-asserted-by":"publisher","first-page":"6107","DOI":"10.1109\/TIP.2021.3089909","volume":"30","author":"M Cao","year":"2021","unstructured":"Cao M et al (2021) UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing. IEEE Trans Image Process 30:6107\u20136116. https:\/\/doi.org\/10.1109\/TIP.2021.3089909","journal-title":"IEEE Trans Image Process"},{"key":"9288_CR102","doi-asserted-by":"publisher","unstructured":"Liang H, Hou X, and Shen L (2021) \u201cSSFlow: style-guided neural spline flows for face image manipulation,\u201d MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia, pp. 79\u201387, https:\/\/doi.org\/10.1145\/3474085.3475454.","DOI":"10.1145\/3474085.3475454"},{"key":"9288_CR103","doi-asserted-by":"publisher","unstructured":"Karras T, Laine S, and Aila T (2019) \u201cA style-based generator architecture for generative adversarial networks,\u201d Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4396\u20134405, https:\/\/doi.org\/10.1109\/CVPR.2019.00453.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"9288_CR104","doi-asserted-by":"publisher","unstructured":"Tong X, Wang L, Pan X, and Wang JG (2020) \u201cAn Overview of Deepfake: The Sword of Damocles in AI,\u201d Proceedings - 2020 International Conference on Computer Vision, Image and Deep Learning, CVIDL 2020, pp. 265\u2013273, https:\/\/doi.org\/10.1109\/CVIDL51233.2020.00-88.","DOI":"10.1109\/CVIDL51233.2020.00-88"},{"key":"9288_CR105","doi-asserted-by":"publisher","unstructured":"Wu W, Zhou W, Zhang W, Fang H, and Yu N (2022) \u201cCapturing the lighting inconsistency for deepfake detection,\u201d lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13339 LNCS, pp. 637\u2013647, https:\/\/doi.org\/10.1007\/978-3-031-06788-4_52.","DOI":"10.1007\/978-3-031-06788-4_52"},{"key":"9288_CR106","volume":"42","author":"T Wang","year":"2022","unstructured":"Wang T, Liu M, Cao W, Chow KP (2022) Deepfake noise investigation and detection. Forensic Sci Int 42:301395.","journal-title":"Forensic Sci Int"},{"key":"9288_CR107","doi-asserted-by":"publisher","DOI":"10.1016\/J.ESWA.2022.118423","volume":"210","author":"S Ganguly","year":"2022","unstructured":"Ganguly S, Ganguly A, Mohiuddin S, Malakar S, Sarkar R (2022) ViXNet: vision transformer with xception network for deepfakes based video and image forgery detection. Expert Syst Appl 210:118423. https:\/\/doi.org\/10.1016\/J.ESWA.2022.118423","journal-title":"Expert Syst Appl"},{"key":"9288_CR108","doi-asserted-by":"publisher","unstructured":"Matern F, Riess C, and Stamminger M (2019) \u201cExploiting visual artifacts to expose deepfakes and face manipulations,\u201d Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2019, pp. 83\u201392, https:\/\/doi.org\/10.1109\/WACVW.2019.00020.","DOI":"10.1109\/WACVW.2019.00020"},{"key":"9288_CR109","unstructured":"Sabir E, Cheng J, Jaiswal A, AbdAlmageed W, Masi I, and Natarajan P (2019) \u201cRecurrent Convolutional Strategies for Face Manipulation Detection in Videos,\u201d [Online]. Available: http:\/\/arxiv.org\/abs\/1905.00582"},{"key":"9288_CR110","doi-asserted-by":"publisher","DOI":"10.3390\/S21217367","author":"G Lee","year":"2021","unstructured":"Lee G, Kim M (2021) Deepfake Detection using the rate of change between frames based on computer vision. Sensors (Basel). https:\/\/doi.org\/10.3390\/S21217367","journal-title":"Sensors (Basel)"},{"key":"9288_CR111","doi-asserted-by":"crossref","unstructured":"Qi H et al., (2020) \u201cDeepRhythm: exposing deepfakes with attentional visual heartbeat rhythms,\u201d [Online]. Available: http:\/\/arxiv.org\/abs\/2006.07634","DOI":"10.1145\/3394171.3413707"},{"key":"9288_CR112","doi-asserted-by":"crossref","unstructured":"Li Y, Chang MC and Lyu S (2023) \u201cIn Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking,\u201d Jun. 2018, Accessed: May 11, [Online]. Available: https:\/\/arxiv.org\/abs\/1806.02877v2","DOI":"10.1109\/WIFS.2018.8630787"},{"key":"9288_CR113","doi-asserted-by":"publisher","first-page":"83144","DOI":"10.1109\/ACCESS.2020.2988660","volume":"8","author":"T Jung","year":"2020","unstructured":"Jung T, Kim S, Kim K (2020) DeepVision: deepfakes detection using human eye blinking pattern. IEEE Access 8:83144\u201383154. https:\/\/doi.org\/10.1109\/ACCESS.2020.2988660","journal-title":"IEEE Access"},{"key":"9288_CR114","unstructured":"\u201cEyebrow Recognition for Identifying Deepfake Videos | IEEE Conference Publication | IEEE Xplore.\u201d https:\/\/ieeexplore.ieee.org\/document\/9211068 (accessed May 11, 2023)."},{"key":"9288_CR115","unstructured":"Amerini I, Galteri L, Caldelli R, \u2666\u2663, and Del Bimbo A, (2023) \u201cDeepfake Video Detection through Optical Flow based CNN\u201d, Accessed: May 12, [Online]. Available: https:\/\/github.com\/deepfakes\/"},{"key":"9288_CR116","doi-asserted-by":"publisher","unstructured":"McCloskey S and Albright M (2019) \u201cDetecting GAN-generated imagery using saturation cues,\u201d Proceedings - International Conference on Image Processing, ICIP, vol. 2019-September, pp. 4584\u20134588, https:\/\/doi.org\/10.1109\/ICIP.2019.8803661.","DOI":"10.1109\/ICIP.2019.8803661"},{"key":"9288_CR117","doi-asserted-by":"publisher","DOI":"10.3390\/JIMAGING7070102","author":"S Ferreira","year":"2021","unstructured":"Ferreira S, Antunes M, Correia ME (2021) Exposing manipulated photos and videos in digital forensics analysis. J Imaging. https:\/\/doi.org\/10.3390\/JIMAGING7070102","journal-title":"J Imaging"},{"key":"9288_CR118","doi-asserted-by":"publisher","unstructured":"Zhang Y, Zheng L, and Thing VLL (2017) \u201cAutomated face swapping and its detection,\u201d 2017 IEEE 2nd International Conference on Signal and Image Processing, ICSIP 2017, vol. 2017-January, pp. 15\u201319, https:\/\/doi.org\/10.1109\/SIPROCESS.2017.8124497.","DOI":"10.1109\/SIPROCESS.2017.8124497"},{"key":"9288_CR119","doi-asserted-by":"publisher","unstructured":"Mo H, Chen B, and Luo W (2018) \u201cFake faces identification via convolutional neural network,\u201d IH and MMSec 2018 In: Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security, pp. 43\u201347, https:\/\/doi.org\/10.1145\/3206004.3206009.","DOI":"10.1145\/3206004.3206009"},{"key":"9288_CR120","doi-asserted-by":"publisher","unstructured":"L. Nataraj et al., (2019) \u201cDetecting GAN generated Fake Images using Co-occurrence Matrices,\u201d IS and T International Symposium on Electronic Imaging Science and Technology, https:\/\/doi.org\/10.2352\/ISSN.2470-1173.2019.5.MWSF-532.","DOI":"10.2352\/ISSN.2470-1173.2019.5.MWSF-532"},{"key":"9288_CR121","doi-asserted-by":"publisher","unstructured":"Guarnera L, Giudice O, and Battiato S (2020) \u201cDeepFake detection by analyzing convolutional traces,\u201d IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2020-June, pp. 2841\u20132850, https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00341.","DOI":"10.1109\/CVPRW50498.2020.00341"},{"key":"9288_CR122","unstructured":"Li Y and Lyu S (2023) \u201cExposing DeepFake Videos By Detecting Face Warping Artifacts,\u201d Nov. 2018, Accessed: Apr. 13 [Online]. Available: https:\/\/arxiv.org\/abs\/1811.00656v3"},{"key":"9288_CR123","doi-asserted-by":"publisher","unstructured":"Li L et al., (2019) \u201cFace X-ray for More General Face Forgery Detection,\u201d Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 5000\u20135009, https:\/\/doi.org\/10.1109\/CVPR42600.2020.00505.","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"9288_CR124","doi-asserted-by":"publisher","unstructured":"Li K, Yu S, Ji Y, Wang C, Wu and Xue H (2020) \u201cFighting Against Deepfake: Patch&Pair Convolutional Neural Networks (PPCNN),\u201d The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020, pp. 88\u201389, https:\/\/doi.org\/10.1145\/3366424.3382711","DOI":"10.1145\/3366424.3382711"},{"key":"9288_CR125","doi-asserted-by":"publisher","DOI":"10.1016\/J.ASOC.2021.107256","author":"S Lee","year":"2021","unstructured":"Lee S, Tariq S, Shin Y, Woo SS (2021) Detecting handcrafted facial image manipulations and GAN-generated facial images using Shallow-FakeFaceNet. Appl Soft Comput. https:\/\/doi.org\/10.1016\/J.ASOC.2021.107256","journal-title":"Appl Soft Comput"},{"key":"9288_CR126","doi-asserted-by":"publisher","DOI":"10.1016\/J.PATCOG.2021.107950","volume":"116","author":"Z Shang","year":"2021","unstructured":"Shang Z, Xie H, Zha Z, Yu L, Li Y, Zhang Y (2021) PRRNet: Pixel-Region relation network for face forgery detection. Pattern Recognit 116:107950. https:\/\/doi.org\/10.1016\/J.PATCOG.2021.107950","journal-title":"Pattern Recognit"},{"key":"9288_CR127","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.neunet.2023.01.001","volume":"160","author":"J Ke","year":"2023","unstructured":"Ke J, Wang L (2023) DF-UDetector: an effective method towards robust deepfake detection via feature restoration. Neural Netw 160:216\u2013226. https:\/\/doi.org\/10.1016\/j.neunet.2023.01.001","journal-title":"Neural Netw"},{"key":"9288_CR128","doi-asserted-by":"publisher","first-page":"2636","DOI":"10.1016\/J.PROCS.2023.01.237","volume":"218","author":"U Kosarkar","year":"2023","unstructured":"Kosarkar U, Sarkarkar G, Gedam S (2023) Revealing and classification of deepfakes video\u2019s images using a customize convolution neural network model. Procedia Comput Sci 218:2636\u20132652. https:\/\/doi.org\/10.1016\/J.PROCS.2023.01.237","journal-title":"Procedia Comput Sci"},{"key":"9288_CR129","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/J.PATREC.2021.03.005","volume":"146","author":"R Caldelli","year":"2021","unstructured":"Caldelli R, Galteri L, Amerini I, Del Bimbo A (2021) Optical flow based CNN for detection of unlearnt deepfake manipulations. Pattern Recognit Lett 146:31\u201337. https:\/\/doi.org\/10.1016\/J.PATREC.2021.03.005","journal-title":"Pattern Recognit Lett"},{"issue":"1","key":"9288_CR130","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/J.GLTP.2022.04.017","volume":"3","author":"VVVNS Vamsi","year":"2022","unstructured":"Vamsi VVVNS et al (2022) Deepfake detection in digital media forensics. Global Trans Proc 3(1):74\u201379. https:\/\/doi.org\/10.1016\/J.GLTP.2022.04.017","journal-title":"Global Trans Proc"},{"key":"9288_CR131","doi-asserted-by":"publisher","DOI":"10.1016\/J.SOFTX.2022.101115","author":"A Elhassan","year":"2022","unstructured":"Elhassan A, Al-Fawa\u2019reh M, Jafar MT, Ababneh M, Jafar ST (2022) DFT-MF: Enhanced deepfake detection using mouth movement and transfer learning. SoftwareX. https:\/\/doi.org\/10.1016\/J.SOFTX.2022.101115","journal-title":"SoftwareX"},{"key":"9288_CR132","doi-asserted-by":"publisher","unstructured":"Liu B, Liu B, Ding M, Zhu T and Yu X (2023) \u201cTI2Net: Temporal Identity Inconsistency Network for Deepfake Detection,\u201d Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023, pp. 4680\u20134689, https:\/\/doi.org\/10.1109\/WACV56688.2023.00467.","DOI":"10.1109\/WACV56688.2023.00467"},{"key":"9288_CR133","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1007\/s42979-020-00225-9","volume":"1","author":"A Singh","year":"2020","unstructured":"Singh A, Saimbhi AS, Singh N, Mittal M (2020) DeepFake Video Detection: A Time-Distributed Approach. SN Comput Sci 1:4. https:\/\/doi.org\/10.1007\/s42979-020-00225-9","journal-title":"SN Comput Sci"},{"key":"9288_CR134","doi-asserted-by":"publisher","unstructured":"Sun Z, Han Y, Hua Z, Ruan N, and Jia W (2021) \u201cImproving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Features,\u201d 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3608\u20133617, https:\/\/doi.org\/10.1109\/CVPR46437.2021.00361.","DOI":"10.1109\/CVPR46437.2021.00361"},{"key":"9288_CR135","unstructured":"Liu H et al., \u201cSpatial-phase shallow learning: rethinking face forgery detection in frequency domain\u201d."},{"key":"9288_CR136","doi-asserted-by":"publisher","first-page":"816","DOI":"10.3390\/MATH11040816","volume":"11","author":"B Wang","year":"2023","unstructured":"Wang B, Wu X, Tang Y, Ma Y, Shan Z, Wei F (2023) Frequency Domain Filtered Residual Network for Deepfake Detection. Mathematics 11:816. https:\/\/doi.org\/10.3390\/MATH11040816","journal-title":"Mathematics"},{"key":"9288_CR137","doi-asserted-by":"publisher","unstructured":"Frank J, Eisenhofer T, Sch\u00f6nherr L, Fischer A, Kolossa D and Holz T (2020) \u201cLeveraging frequency analysis for deep fake image recognition,\u201d ICML\u201920: Proceedings of the 37th International Conference on Machine Learning,  https:\/\/doi.org\/10.5555\/3524938.3525242.","DOI":"10.5555\/3524938.3525242"},{"key":"9288_CR138","unstructured":"Nadimpalli AV and Rattani A \u201cOn Improving Cross-dataset Generalization of Deepfake Detectors.\u201d"},{"key":"9288_CR139","unstructured":"Chen L, Zhang Y, Song Y, Liu L, and Wang J \u201cSelf-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection.\u201d [Online]. Available: https:\/\/github.com\/liangchen527\/SLADD."}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09288-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09288-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09288-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T10:19:39Z","timestamp":1707733179000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09288-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,14]]},"references-count":139,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9288"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09288-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,14]]},"assertion":[{"value":"25 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}}]}}