{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T19:02:44Z","timestamp":1781290964117,"version":"3.54.1"},"reference-count":89,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-00911-7","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T12:06:02Z","timestamp":1751889962000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Audio\u2013Visual Synchronization and Lip Movement Analysis for Real-Time Deepfake Detection"],"prefix":"10.1007","volume":"18","author":[{"given":"Muhammad","family":"Javed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaohui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fida Hussain","family":"Dahri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asif Ali","family":"Laghari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Kraj\u010d\u00edk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad","family":"Almadhor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,7]]},"reference":[{"key":"911_CR1","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, S.M., Khan, A.N.: DeepFake detection for human face images and videos: a survey. IEEE Access 10, 18757\u201318775 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3151186","journal-title":"IEEE Access"},{"key":"911_CR2","doi-asserted-by":"publisher","first-page":"25494","DOI":"10.1109\/ACCESS.2022.3154404","volume":"10","author":"MS Rana","year":"2022","unstructured":"Rana, M.S., Nobi, M.N., Murali, B., Sung, A.H.: Deepfake detection: a systematic literature review. IEEE Access 10, 25494\u201325513 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3154404","journal-title":"IEEE Access"},{"issue":"4","key":"911_CR3","doi-asserted-by":"publisher","first-page":"3974","DOI":"10.1007\/s10489-022-03766-z","volume":"53","author":"M Masood","year":"2023","unstructured":"Masood, M., Nawaz, M., Malik, K.M., Javed, A., Irtaza, A., Malik, H.: Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward. Appl. Intell. 53(4), 3974\u20134026 (2023). https:\/\/doi.org\/10.1007\/s10489-022-03766-z","journal-title":"Appl. Intell."},{"key":"911_CR4","doi-asserted-by":"publisher","first-page":"123493","DOI":"10.1109\/ACCESS.2021.3110859","volume":"9","author":"E Kim","year":"2021","unstructured":"Kim, E., Cho, S.: Exposing fake faces through deep neural networks combining content and trace feature extractors. IEEE Access 9, 123493\u2013123503 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3110859","journal-title":"IEEE Access"},{"key":"911_CR5","doi-asserted-by":"publisher","first-page":"134701","DOI":"10.1109\/ACCESS.2023.3332561","volume":"11","author":"R Mahum","year":"2023","unstructured":"Mahum, R., Irtaza, A., Javed, A.: EDL-Det: a robust TTS synthesis detector using VGG19-based YAMNet and ensemble learning block. IEEE Access 11, 134701\u2013134716 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3332561","journal-title":"IEEE Access"},{"key":"911_CR6","unstructured":"Khalid, H., Tariq, S., Kim, M., Woo, S.S.: FakeAVCeleb: a novel audio-video multimodal deepfake dataset (2021). http:\/\/arxiv.org\/abs\/2108.05080"},{"key":"911_CR7","doi-asserted-by":"publisher","first-page":"105344","DOI":"10.1109\/ACCESS.2023.3317897","volume":"11","author":"C Aviles-Cruz","year":"2023","unstructured":"Aviles-Cruz, C., Celis-Escudero, G.J.: 3G-AN: triple-generative adversarial network under corse-medium-fine generator architecture. IEEE Access 11, 105344\u2013105354 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3317897","journal-title":"IEEE Access"},{"key":"911_CR8","doi-asserted-by":"publisher","unstructured":"Rana, M.S., Sung, A.H.: DeepfakeStack: a deep ensemble-based learning technique for deepfake detection.. In: Proceedings\u20142020 7th IEEE International Conference on Cyber Security and Cloud Computing. 2020 6th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2020, pp. 70\u201375 (2020). https:\/\/doi.org\/10.1109\/CSCloud-EdgeCom49738.2020.00021","DOI":"10.1109\/CSCloud-EdgeCom49738.2020.00021"},{"key":"911_CR9","doi-asserted-by":"crossref","unstructured":"Lee, K., Zhang, Y., Duan, Z.: A multi-stream fusion approach with one-class learning for audio-visual deepfake detection (2024). http:\/\/arxiv.org\/abs\/2406.14176","DOI":"10.1109\/MMSP61759.2024.10743671"},{"key":"911_CR10","doi-asserted-by":"publisher","unstructured":"Chen, P., et al.: FSSPOTTER: spotting face-swapped video by spatial and temporal clues. In: Proceedings\u2014IEEE International Conference on Multimedia and Expo, vol. 2020-July (2020). https:\/\/doi.org\/10.1109\/ICME46284.2020.9102914","DOI":"10.1109\/ICME46284.2020.9102914"},{"issue":"2","key":"911_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.9734\/ajarr\/2024\/v18i2601","volume":"18","author":"TO Oladoyinbo","year":"2024","unstructured":"Oladoyinbo, T.O., Olabanji, S.O., Olaniyi, O.O., Adebiyi, O.O., Okunleye, O.J., Alao, A.I.: Exploring the challenges of artificial intelligence in data integrity and its influence on social dynamics. Asian J. Adv. Res. Reports 18(2), 1\u201323 (2024). https:\/\/doi.org\/10.9734\/ajarr\/2024\/v18i2601","journal-title":"Asian J. Adv. Res. Reports"},{"key":"911_CR12","doi-asserted-by":"publisher","first-page":"59204","DOI":"10.1109\/ACCESS.2023.3285826","volume":"11","author":"MO Alassafi","year":"2023","unstructured":"Alassafi, M.O., et al.: A novel deep learning architecture with image diffusion for robust face presentation attack detection. IEEE Access 11, 59204\u201359216 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3285826","journal-title":"IEEE Access"},{"key":"911_CR13","doi-asserted-by":"publisher","first-page":"27069","DOI":"10.1109\/ACCESS.2022.3157724","volume":"10","author":"W Shahid","year":"2022","unstructured":"Shahid, W., Li, Y., Staples, D., Amin, G., Hakak, S., Ghorbani, A.: Are you a cyborg, bot or human?\u2014a survey on detecting fake news spreaders. IEEE Access 10, 27069\u201327083 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3157724","journal-title":"IEEE Access"},{"key":"911_CR14","doi-asserted-by":"publisher","unstructured":"Thies, J., Elgharib, M., Tewari, A., Theobalt, C., Nie\u00dfner, M.: Neural voice puppetry: audio-driven facial reenactment. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12361 LNCS, pp. 716\u2013731 (2020) https:\/\/doi.org\/10.1007\/978-3-030-58517-4_42","DOI":"10.1007\/978-3-030-58517-4_42"},{"key":"911_CR15","doi-asserted-by":"publisher","unstructured":"Jia, S., Li, X., Lyu, S.: Model attribution of face-swap deepfake videos. In: Proceedigngs\u2014International Conference on Image Processing ICIP, pp. 2356\u20132360 (2022). https:\/\/doi.org\/10.1109\/ICIP46576.2022.9897972","DOI":"10.1109\/ICIP46576.2022.9897972"},{"key":"911_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107185","author":"MZ Uddin","year":"2024","unstructured":"Uddin, M.Z., Shahriar, M.A., Mahamood, M.N., Alnajjar, F., Pramanik, M.I., Ahad, M.A.R.: Deep learning with image-based autism spectrum disorder analysis: a systematic review. Eng. Appl. Artif. Intell. (2024). https:\/\/doi.org\/10.1016\/j.engappai.2023.107185","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"4","key":"911_CR17","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1080\/23268743.2020.1765851","volume":"7","author":"K Kikerpill","year":"2020","unstructured":"Kikerpill, K.: Choose your stars and studs: the rise of deepfake designer porn. Porn Stud. 7(4), 352\u2013356 (2020). https:\/\/doi.org\/10.1080\/23268743.2020.1765851","journal-title":"Porn Stud."},{"key":"911_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.103860","author":"K Jayashre","year":"2024","unstructured":"Jayashre, K., Amsaprabhaa, M.: Safeguarding media integrity: a hybrid optimized deep feature fusion based deepfake detection in videos. Comput. Secur. (2024). https:\/\/doi.org\/10.1016\/j.cose.2024.103860","journal-title":"Comput. Secur."},{"key":"911_CR19","unstructured":"Patil, K., Kale, S., Dhokey, J., Gulhane, A.: Deepfake detection using biological features: a survey (2023). http:\/\/arxiv.org\/abs\/2301.05819"},{"key":"911_CR20","doi-asserted-by":"publisher","unstructured":"Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: MesoNet: a compact facial video forgery detection network. In: 10th International Symposium on Digital Forensics and Security WIFS 2018 (2018). https:\/\/doi.org\/10.1109\/WIFS.2018.8630761","DOI":"10.1109\/WIFS.2018.8630761"},{"issue":"5","key":"911_CR21","doi-asserted-by":"publisher","first-page":"4925","DOI":"10.1007\/s12652-020-01932-0","volume":"12","author":"M Arun Anoop","year":"2021","unstructured":"Arun Anoop, M., Poonkuntran, S.: LPG: a novel approach for medical forgery detection in image transmission. J. Ambient Intell. Human. Comput. 12(5), 4925\u20134941 (2021). https:\/\/doi.org\/10.1007\/s12652-020-01932-0","journal-title":"J. Ambient Intell. Human. Comput."},{"issue":"S7","key":"911_CR22","doi-asserted-by":"publisher","first-page":"59","DOI":"10.14733\/cadaps.2024.S7.59-73","volume":"21","author":"T Wang","year":"2024","unstructured":"Wang, T., Wu, D.: Computer-aided traditional art design based on artificial intelligence and human-computer interaction. Comput. Aided. Des. Appl. 21(S7), 59\u201373 (2024). https:\/\/doi.org\/10.14733\/cadaps.2024.S7.59-73","journal-title":"Comput. Aided. Des. Appl."},{"issue":"42","key":"911_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5120\/ijais2023451952","volume":"12","author":"EJ Aloke","year":"2023","unstructured":"Aloke, E.J., Abah, J.: Enhancing the fight against social media misinformation: an ensemble deep learning framework for detecting deepfakes. Int. J. Appl. Inf. Syst. 12(42), 1\u201314 (2023). https:\/\/doi.org\/10.5120\/ijais2023451952","journal-title":"Int. J. Appl. Inf. Syst."},{"key":"911_CR24","doi-asserted-by":"publisher","first-page":"152788","DOI":"10.1109\/ACCESS.2019.2947855","volume":"7","author":"M Al-Sarem","year":"2019","unstructured":"Al-Sarem, M., Boulila, W., Al-Harby, M., Qadir, J., Alsaeedi, A.: Deep learning-based rumor detection on microblogging platforms: a systematic review. IEEE Access 7, 152788\u2013152812 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2947855","journal-title":"IEEE Access"},{"key":"911_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124260","author":"F Abbas","year":"2024","unstructured":"Abbas, F., Taeihagh, A.: Unmasking deepfakes: a systematic review of deepfake detection and generation techniques using artificial intelligence. Expert Syst. Appl. (2024). https:\/\/doi.org\/10.1016\/j.eswa.2024.124260","journal-title":"Expert Syst. Appl."},{"key":"911_CR26","doi-asserted-by":"publisher","first-page":"133","DOI":"10.4018\/979-8-3693-1902-4.ch009","volume-title":"Leveraging AI and Emotional Intelligence in Contemporary Business Organizations","author":"R Thakur","year":"2023","unstructured":"Thakur, R.: Introduction to artificial intelligence and its importance in modern business management. In: Leveraging AI and Emotional Intelligence in Contemporary Business Organizations, pp. 133\u2013165. IGI, Hershey (2023). https:\/\/doi.org\/10.4018\/979-8-3693-1902-4.ch009"},{"key":"911_CR27","doi-asserted-by":"publisher","first-page":"120766","DOI":"10.1109\/ACCESS.2023.3328210","volume":"11","author":"M Oulad-Kaddour","year":"2023","unstructured":"Oulad-Kaddour, M., Haddadou, H., Vilda, C.C., Palacios-Alonso, D., Benatchba, K., Cabello, E.: Deep learning-based gender classification by training with fake data. IEEE Access 11, 120766\u2013120779 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3328210","journal-title":"IEEE Access"},{"key":"911_CR28","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1520","author":"A Heidari","year":"2024","unstructured":"Heidari, A., Jafari Navimipour, N., Dag, H., Unal, M.: Deepfake detection using deep learning methods: a systematic and comprehensive review. Wiley Interdiscip Rev. Data Min. Knowl. Discov. (2024). https:\/\/doi.org\/10.1002\/widm.1520","journal-title":"Wiley Interdiscip Rev. Data Min. Knowl. Discov."},{"key":"911_CR29","doi-asserted-by":"publisher","first-page":"1880","DOI":"10.1109\/ACCESS.2023.3348450","volume":"12","author":"SA Khan","year":"2024","unstructured":"Khan, S.A., Dang-Nguyen, D.T.: Deepfake detection: analyzing model generalization across architectures, datasets, and pre-training paradigms. IEEE Access 12, 1880\u20131908 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2023.3348450","journal-title":"IEEE Access"},{"issue":"2","key":"911_CR30","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1109\/TCDS.2021.3064679","volume":"14","author":"W Liu","year":"2022","unstructured":"Liu, W., Wei, X., Lei, T., Wang, X., Meng, H., Nandi, A.K.: Data-fusion-based two-stage cascade framework for multimodality face anti-spoofing. IEEE Trans. Cogn. Dev. Syst. 14(2), 672\u2013683 (2022). https:\/\/doi.org\/10.1109\/TCDS.2021.3064679","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"911_CR31","doi-asserted-by":"publisher","unstructured":"Hashmi, A., Shahzad, S.A., Ahmad, W., Lin, C.W., Tsao, Y., Wang, H.M.: Multimodal forgery detection using ensemble learning. In: Proceedings of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference APSIPA ASC 2022, pp. 1524\u20131532 (2022). https:\/\/doi.org\/10.23919\/APSIPAASC55919.2022.9980255","DOI":"10.23919\/APSIPAASC55919.2022.9980255"},{"key":"911_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110124","author":"H Ilyas","year":"2023","unstructured":"Ilyas, H., Javed, A., Malik, K.M.: AVFakeNet: a unified end-to-end dense swin transformer deep learning model for audio\u2013visual deepfakes detection. Appl. Soft Comput. (2023). https:\/\/doi.org\/10.1016\/j.asoc.2023.110124","journal-title":"Appl. Soft Comput."},{"issue":"5","key":"911_CR33","doi-asserted-by":"publisher","first-page":"3557","DOI":"10.1109\/TPAMI.2024.3350004","volume":"46","author":"A Melnik","year":"2024","unstructured":"Melnik, A., et al.: Face generation and editing with StyleGAN: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 46(5), 3557\u20133576 (2024). https:\/\/doi.org\/10.1109\/TPAMI.2024.3350004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"911_CR34","doi-asserted-by":"publisher","unstructured":"Liang, T., et al.: SDHF: spotting DeepFakes with hierarchical features. In: Proceedings\u2014 International Conference on Tools with Artificial Intelligence, ICTAI, vol. 2020-November, pp. 675\u2013680 (2020). https:\/\/doi.org\/10.1109\/ICTAI50040.2020.00108","DOI":"10.1109\/ICTAI50040.2020.00108"},{"key":"911_CR35","doi-asserted-by":"publisher","unstructured":"Thomas, J.M., Ebenezer, V., Richard, R.P.: A deepfake image classifier system for real and doctored image differentiation. In: 7th International Conference on Innovative Computing Technologies, ICICT 2024, pp. 1247\u20131251 (2024). https:\/\/doi.org\/10.1109\/ICICT60155.2024.10544898","DOI":"10.1109\/ICICT60155.2024.10544898"},{"key":"911_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.7717\/PEERJ-CS.2037","volume":"10","author":"SM Qureshi","year":"2024","unstructured":"Qureshi, S.M., Saeed, A., Almotiri, S.H., Ahmad, F., Ghamdi, M.A.A.: Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media. PeerJ Comput. Sci. 10, 1\u201340 (2024). https:\/\/doi.org\/10.7717\/PEERJ-CS.2037","journal-title":"PeerJ Comput. Sci."},{"key":"911_CR37","doi-asserted-by":"publisher","unstructured":"Khalid, H., Kim, M., Tariq, S., Woo, S.S.: Evaluation of an audio-video multimodal deepfake dataset using unimodal and multimodal detectors. In: ADGD 2021\u2014Proceedings of the 1st Workshop on Synthetic Multimedia\u2014Audiov. Deep. Gener. Detect. co-located with ACM MM 2021, pp. 7\u201315 (2021). https:\/\/doi.org\/10.1145\/3476099.3484315","DOI":"10.1145\/3476099.3484315"},{"key":"911_CR38","doi-asserted-by":"publisher","first-page":"78901","DOI":"10.1109\/ACCESS.2020.2987966","volume":"8","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Hu, R., Li, D., Wang, X.: Fake identity attributes detection based on analysis of natural and human behaviors. IEEE Access 8, 78901\u201378911 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2987966","journal-title":"IEEE Access"},{"key":"911_CR39","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. Fusion 64, 131\u2013148 (2020). https:\/\/doi.org\/10.1016\/j.inffus.2020.06.014","journal-title":"Inf. Fusion"},{"key":"911_CR40","doi-asserted-by":"publisher","DOI":"10.3390\/jsan12040061","author":"MSH Mukta","year":"2023","unstructured":"Mukta, M.S.H., et al.: An investigation of the effectiveness of deepfake models and tools. J. Sens. Actuator Netw. (2023). https:\/\/doi.org\/10.3390\/jsan12040061","journal-title":"J. Sens. Actuator Netw."},{"key":"911_CR41","doi-asserted-by":"publisher","first-page":"201635","DOI":"10.1109\/ACCESS.2020.3035747","volume":"8","author":"S Lim","year":"2020","unstructured":"Lim, S., Gwak, Y., Kim, W., Roh, J.H., Cho, S.: One-class learning method based on live correlation loss for face anti-spoofing. IEEE Access 8, 201635\u2013201648 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3035747","journal-title":"IEEE Access"},{"key":"911_CR42","doi-asserted-by":"publisher","unstructured":"Kumar, P., Vatsa, M., Singh, R.: Detecting Face2Face facial reenactment in videos. In: Proceedings\u20142020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, pp. 2578\u20132586 (2020). https:\/\/doi.org\/10.1109\/WACV45572.2020.9093628","DOI":"10.1109\/WACV45572.2020.9093628"},{"key":"911_CR43","doi-asserted-by":"publisher","unstructured":"Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Niessner, M.: FaceForensics++: learning to detect manipulated facial images. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2019-October, pp. 1\u201311 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00009","DOI":"10.1109\/ICCV.2019.00009"},{"key":"911_CR44","unstructured":"FaceForensics++: learning to detect manipulated facial images"},{"issue":"11","key":"911_CR45","doi-asserted-by":"publisher","first-page":"39","DOI":"10.22215\/TIMREVIEW\/1282","volume":"9","author":"M Westerlund","year":"2019","unstructured":"Westerlund, M.: The emergence of deepfake technology: a review. Technol. Innov. Manag. Rev. 9(11), 39\u201352 (2019). https:\/\/doi.org\/10.22215\/TIMREVIEW\/1282","journal-title":"Technol. Innov. Manag. Rev."},{"key":"911_CR46","doi-asserted-by":"publisher","unstructured":"Lyu, S.: Deepfake detection: current challenges and next steps. In: 2020 IEEE International Conference on Multimedia and Expo Work. ICMEW 2020 (2020). https:\/\/doi.org\/10.1109\/ICMEW46912.2020.9105991","DOI":"10.1109\/ICMEW46912.2020.9105991"},{"issue":"1","key":"911_CR47","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.gltp.2022.04.017","volume":"3","author":"VVVNS Vamsi","year":"2022","unstructured":"Vamsi, V.V.V.N.S., et al.: Deepfake detection in digital media forensics. Glob. Transit. Proc. 3(1), 74\u201379 (2022). https:\/\/doi.org\/10.1016\/j.gltp.2022.04.017","journal-title":"Glob. Transit. Proc."},{"key":"911_CR48","doi-asserted-by":"publisher","unstructured":"Guera, D., Delp, E.J.: Deepfake video detection using recurrent neural networks. In: Proceedings of AVSS 2018\u20142018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (2018). https:\/\/doi.org\/10.1109\/AVSS.2018.8639163","DOI":"10.1109\/AVSS.2018.8639163"},{"issue":"1","key":"911_CR49","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1080\/23080477.2023.2268380","volume":"12","author":"M Rehaan","year":"2024","unstructured":"Rehaan, M., Kaur, N., Kingra, S.: Face manipulated deepfake generation and recognition approaches: a survey. Smart Sci. 12(1), 53\u201373 (2024). https:\/\/doi.org\/10.1080\/23080477.2023.2268380","journal-title":"Smart Sci."},{"issue":"1","key":"911_CR50","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1177\/15248380221143772","volume":"25","author":"K Mania","year":"2024","unstructured":"Mania, K.: Legal protection of revenge and deepfake porn victims in the european union: findings from a comparative legal study. Trauma Violence Abus. 25(1), 117\u2013129 (2024). https:\/\/doi.org\/10.1177\/15248380221143772","journal-title":"Trauma Violence Abus."},{"key":"911_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.jretconser.2023.103560","author":"J Yu","year":"2024","unstructured":"Yu, J., Dickinger, A., So, K.K.F., Egger, R.: Artificial intelligence-generated virtual influencer: examining the effects of emotional display on user engagement. J. Retail. Consum. Serv. (2024). https:\/\/doi.org\/10.1016\/j.jretconser.2023.103560","journal-title":"J. Retail. Consum. Serv."},{"key":"911_CR52","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2024.3352049","author":"Z Yu","year":"2024","unstructured":"Yu, Z., Cai, R., Li, Z., Yang, W., Shi, J., Kot, A.C.: Benchmarking joint face spoofing and forgery detection with visual and physiological cues. IEEE Trans. Depend. Secur. Comput. (2024). https:\/\/doi.org\/10.1109\/TDSC.2024.3352049","journal-title":"IEEE Trans. Depend. Secur. Comput."},{"key":"911_CR53","doi-asserted-by":"publisher","first-page":"117865","DOI":"10.1109\/ACCESS.2023.3324403","volume":"11","author":"S Waseem","year":"2023","unstructured":"Waseem, S., Abu Bakar, S.A.R.S., Ahmed, B.A., Omar, Z., Eisa, T.A.E., Dalam, M.E.E.: DeepFake on face and expression swap: a review. IEEE Access 11, 117865\u2013117906 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3324403","journal-title":"IEEE Access"},{"key":"911_CR54","doi-asserted-by":"publisher","first-page":"1741","DOI":"10.1109\/TIFS.2022.3169921","volume":"17","author":"C Kong","year":"2022","unstructured":"Kong, C., Chen, B., Li, H., Wang, S., Rocha, A., Kwong, S.: Detect and locate: exposing face manipulation by semantic- and noise-level telltales. IEEE Trans. Inf. Forensics Secur. 17, 1741\u20131756 (2022). https:\/\/doi.org\/10.1109\/TIFS.2022.3169921","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"911_CR55","unstructured":"Miao, C., et al.: Multi-spectral class center network for face manipulation detection and localization (2023). http:\/\/arxiv.org\/abs\/2305.10794"},{"key":"911_CR56","doi-asserted-by":"publisher","first-page":"1168","DOI":"10.1109\/TIFS.2023.3332218","volume":"19","author":"A Luo","year":"2024","unstructured":"Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: mining critical clues for general face forgery detection. IEEE Trans. Inf. Forensics Secur. 19, 1168\u20131182 (2024). https:\/\/doi.org\/10.1109\/TIFS.2023.3332218","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"911_CR57","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging9060122","author":"D Salvi","year":"2023","unstructured":"Salvi, D., et al.: A robust approach to multimodal deepfake detection. J. Imaging (2023). https:\/\/doi.org\/10.3390\/jimaging9060122","journal-title":"J. Imaging"},{"key":"911_CR58","doi-asserted-by":"publisher","unstructured":"Gandhi, K., Kulkarni, P., Shah, T., Chaudhari, P., Narvekar, M., Ghag, K.: A multimodal framework for deepfake detection (2024) https:\/\/doi.org\/10.53555\/jes.v20i10s.6126","DOI":"10.53555\/jes.v20i10s.6126"},{"key":"911_CR59","doi-asserted-by":"publisher","DOI":"10.3390\/electronics13152947","author":"M Javed","year":"2024","unstructured":"Javed, M., Zhang, Z., Dahri, F.H., Laghari, A.A.: Real-time deepfake video detection using eye movement analysis with a hybrid deep learning approach. Electronics (2024). https:\/\/doi.org\/10.3390\/electronics13152947","journal-title":"Electronics"},{"key":"911_CR60","doi-asserted-by":"publisher","DOI":"10.3390\/app12199820","author":"A Raza","year":"2022","unstructured":"Raza, A., Munir, K., Almutairi, M.: A novel deep learning approach for deepfake image detection. Appl. Sci. (2022). https:\/\/doi.org\/10.3390\/app12199820","journal-title":"Appl. Sci."},{"key":"911_CR61","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging8100263","author":"L Guarnera","year":"2022","unstructured":"Guarnera, L., et al.: The face deepfake detection challenge. J. Imaging (2022). https:\/\/doi.org\/10.3390\/jimaging8100263","journal-title":"J. Imaging"},{"key":"911_CR62","doi-asserted-by":"publisher","unstructured":"Montserrat, D.M., et al.: Deepfakes detection with automatic face weighting. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2020-June, pp. 2851\u20132859 (2020). https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00342","DOI":"10.1109\/CVPRW50498.2020.00342"},{"key":"911_CR63","doi-asserted-by":"publisher","unstructured":"Weerawardana, M.C., Fernando, T.G.I.: Deepfakes detection methods: a literature survey. In: 2021 International Conference on Information and Automation for Sustainability, ICIAfS 2021, pp. 76\u201381 (2021). https:\/\/doi.org\/10.1109\/ICIAfS52090.2021.9606067","DOI":"10.1109\/ICIAfS52090.2021.9606067"},{"key":"911_CR64","doi-asserted-by":"publisher","unstructured":"Cozzolino, D., Pianese, A., Nie\u00dfner, M., Verdoliva, L.: Audio-visual person-of-interest deepfake detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops, vol. 2023-June, pp. 943\u2013952 (2023). https:\/\/doi.org\/10.1109\/CVPRW59228.2023.00101","DOI":"10.1109\/CVPRW59228.2023.00101"},{"key":"911_CR65","doi-asserted-by":"publisher","unstructured":"Pan, D., Sun, L., Wang, R., Zhang, X., Sinnott, R.O.: Deepfake detection through deep learning. In: Proceeding\u20142020 IEEE\/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2020, pp. 134\u2013143 (2020). https:\/\/doi.org\/10.1109\/BDCAT50828.2020.00001","DOI":"10.1109\/BDCAT50828.2020.00001"},{"key":"911_CR66","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.: DeepVision: deepfakes detection using human eye blinking pattern. IEEE Access 8, 83144\u201383154 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2988660","journal-title":"IEEE Access"},{"key":"911_CR67","doi-asserted-by":"publisher","unstructured":"Feng, K., Wu, J., Tian, M.: A detect method for deepfake video based on full face recognition. In: Proceedings\u20142020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence. ICIBA 2020, pp. 1121\u20131125 (2020). https:\/\/doi.org\/10.1109\/ICIBA50161.2020.9277303","DOI":"10.1109\/ICIBA50161.2020.9277303"},{"key":"911_CR68","doi-asserted-by":"publisher","unstructured":"Patel, M., Gupta, A., Tanwar, S., Obaidat, M.S.: Trans-DF: a transfer learning-based end-to-end deepfake detector. In: 2020 IEEE 5th International Conference on Computing Communication and Automation ICCCA 2020, pp. 796\u2013801 (2020). https:\/\/doi.org\/10.1109\/ICCCA49541.2020.9250803","DOI":"10.1109\/ICCCA49541.2020.9250803"},{"key":"911_CR69","doi-asserted-by":"publisher","unstructured":"Agarwal, S., Farid, H., Fried, O., Agrawala, M.: Detecting deep-fake videos from phoneme-viseme mismatches. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, vol. 2020-June, pp. 2814\u20132822 (2020). https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00338","DOI":"10.1109\/CVPRW50498.2020.00338"},{"key":"911_CR70","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 2018 (2018). https:\/\/doi.org\/10.23919\/BIOSIG.2018.8553251","DOI":"10.23919\/BIOSIG.2018.8553251"},{"key":"911_CR71","doi-asserted-by":"publisher","unstructured":"Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, IEEE International Conference on Acoustics, Speech, and Signal Processing\u2014Proceedings, vol. 2019-May, pp. 8261\u20138265 (2019). https:\/\/doi.org\/10.1109\/ICASSP.2019.8683164","DOI":"10.1109\/ICASSP.2019.8683164"},{"key":"911_CR72","unstructured":"Korshunov, P., Marcel, S.: DeepFakes: a new threat to face recognition? Assessment and detection (2018). http:\/\/arxiv.org\/abs\/1812.08685"},{"key":"911_CR73","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: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1\u201311 (2019). https:\/\/ieeexplore.ieee.org\/document\/9010912\/","DOI":"10.1109\/ICCV.2019.00009"},{"key":"911_CR74","doi-asserted-by":"publisher","unstructured":"Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-DF: a large-scale challenging dataset for DeepFake forensics. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3204\u20133213 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00327","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"911_CR75","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: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2886\u20132895 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00296","DOI":"10.1109\/CVPR42600.2020.00296"},{"key":"911_CR76","unstructured":"Dolhansky, B., et al.: The DeepFake detection challenge (DFDC) Dataset (2020). http:\/\/arxiv.org\/abs\/2006.07397"},{"key":"911_CR77","doi-asserted-by":"publisher","unstructured":"Kwon, P., You, J., Nam, G., Park, S., Chae, G.: KoDF: a large-scale Korean DeepFake detection dataset. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 10724\u201310733 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.01057","DOI":"10.1109\/ICCV48922.2021.01057"},{"key":"911_CR78","doi-asserted-by":"publisher","unstructured":"Chung, J.S., Nagrani, A., Zisserman, A.: VoxceleB2: deep speaker recognition. In: Proceedings of the Annual Conference of the International Speech Communication Association INTERSPEECH, vol. 2018-September, pp. 1086\u20131090 (2018). https:\/\/doi.org\/10.21437\/Interspeech.2018-1929","DOI":"10.21437\/Interspeech.2018-1929"},{"key":"911_CR79","doi-asserted-by":"publisher","unstructured":"Zhou, Y., Lim, S.N.: Joint audio-visual deepfake detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 14780\u201314789 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.01453","DOI":"10.1109\/ICCV48922.2021.01453"},{"key":"911_CR80","doi-asserted-by":"publisher","unstructured":"Shahzad, S.A., Hashmi, A., Khan, S., Peng, Y.T., Tsao, Y., Wang, H.M.: Lip sync matters: a novel multimodal forgery detector. In: Proceedings of 2022 Asia Pacific Signal and Information Processing Association. Annual Summit and Conference. APSIPA ASC 2022, pp. 1885\u20131892 (2022). https:\/\/doi.org\/10.23919\/APSIPAASC55919.2022.9980296","DOI":"10.23919\/APSIPAASC55919.2022.9980296"},{"key":"911_CR81","doi-asserted-by":"publisher","unstructured":"Cai, Z., Stefanov, K., Dhall, A., Hayat, M.: Do you really mean that? Content driven audio-visual deepfake dataset and multimodal method for temporal forgery localization. In: 2022 International Conference on Digital Image Computing: Techniques and Applications DICTA 2022 (2022). https:\/\/doi.org\/10.1109\/DICTA56598.2022.10034605","DOI":"10.1109\/DICTA56598.2022.10034605"},{"key":"911_CR82","doi-asserted-by":"publisher","unstructured":"Zhang, R., Wang, H., Du, M., Liu, H., Zhou, Y., Zeng, Q.: UMMAFormer: a universal multimodal-adaptive transformer framework for temporal forgery localization. In: MM 2023\u201431st ACM International Conference on Multimedia, pp. 8749\u20138759 (2023). https:\/\/doi.org\/10.1145\/3581783.3613767","DOI":"10.1145\/3581783.3613767"},{"key":"911_CR83","doi-asserted-by":"crossref","unstructured":"Cai, Z., et al.: AV-Deepfake1M: a large-scale LLM-driven audio-visual deepfake dataset (2023). http:\/\/arxiv.org\/abs\/2311.15308","DOI":"10.1145\/3664647.3680795"},{"key":"911_CR84","doi-asserted-by":"publisher","unstructured":"Katamneni, V.S., Rattani, A.: MIS-AVoiDD: modality invariant and specific representation for audio-visual deepfake detection. In: Proceedings\u201422nd IEEE International Conference on Machine Learning and Applications ICMLA 2023 (2023). pp. 1371\u20131378. https:\/\/doi.org\/10.1109\/ICMLA58977.2023.00207","DOI":"10.1109\/ICMLA58977.2023.00207"},{"key":"911_CR85","doi-asserted-by":"publisher","unstructured":"Haliassos, A., Vougioukas, K., Petridis, S., Pantic, M.: Lips don\u2019t lie: a generalisable and robust approach to face forgery detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5037\u20135047 (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.00500","DOI":"10.1109\/CVPR46437.2021.00500"},{"key":"911_CR86","unstructured":"Tian, M., Khayatkhoei, M., Mathai, J., AbdAlmageed, W.: Unsupervised multimodal deepfake detection using intra- and cross-modal inconsistencies (2023). http:\/\/arxiv.org\/abs\/2311.17088"},{"key":"911_CR87","doi-asserted-by":"publisher","DOI":"10.1145\/3625231","author":"H Cheng","year":"2023","unstructured":"Cheng, H., Guo, Y., Wang, T., Li, Q., Chang, X., Nie, L.: Voice-face homogeneity tells deepfake. ACM Trans. Multimed. Comput. Commun. Appl. (2023). https:\/\/doi.org\/10.1145\/3625231","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"911_CR88","doi-asserted-by":"publisher","unstructured":"Anas Raza, M., Mahmood Malik, K.: Multimodaltrace: deepfake detection using audio-visual representation learning. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshpos, vol. 2023-June, pp. 993\u20131000 (2023). https:\/\/doi.org\/10.1109\/CVPRW59228.2023.00106","DOI":"10.1109\/CVPRW59228.2023.00106"},{"issue":"1","key":"911_CR89","doi-asserted-by":"publisher","first-page":"407","DOI":"10.14569\/IJACSA.2023.0140144","volume":"14","author":"M Elpeltagy","year":"2023","unstructured":"Elpeltagy, M., Ismail, A., Zaki, M.S., Eldahshan, K.: A novel smart deepfake video detection system. Int. J. Adv. Comput. Sci. Appl. 14(1), 407\u2013419 (2023). https:\/\/doi.org\/10.14569\/IJACSA.2023.0140144","journal-title":"Int. J. Adv. Comput. Sci. Appl."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00911-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-00911-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00911-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T12:06:05Z","timestamp":1751889965000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-00911-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,7]]},"references-count":89,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["911"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-00911-7","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,7]]},"assertion":[{"value":"27 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors did not have any conflict of interest","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"170"}}