{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:27:45Z","timestamp":1791851265367,"version":"4.3.4"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,9,5]],"date-time":"2021-09-05T00:00:00Z","timestamp":1630800000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,5]],"date-time":"2021-09-05T00:00:00Z","timestamp":1630800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Deanship of Scientific Research, Taif University Researchers Supporting Project","award":["TURSP-2020\/08"],"award-info":[{"award-number":["TURSP-2020\/08"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s00521-021-06416-6","type":"journal-article","created":{"date-parts":[[2021,9,5]],"date-time":"2021-09-05T18:02:16Z","timestamp":1630864936000},"page":"1251-1268","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["An efficient cybersecurity framework for facial video forensics detection based on multimodal deep learning"],"prefix":"10.1007","volume":"34","author":[{"given":"Ahmed","family":"Sedik","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Osama S.","family":"Faragallah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hala S.","family":"El-sayed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghada M.","family":"El-Banby","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fathi E. Abd","family":"El-Samie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashraf A. M.","family":"Khalaf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Walid","family":"El-Shafai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,5]]},"reference":[{"key":"6416_CR1","doi-asserted-by":"crossref","unstructured":"Scarf\u00f2, A. (2018). The cyber security challenges in the IoT era. In: Security and resilience in intelligent data-centric systems and communication networks. Academic Press, 53\u201376","DOI":"10.1016\/B978-0-12-811373-8.00003-3"},{"key":"6416_CR2","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.ins.2019.09.038","volume":"511","author":"B Xiao","year":"2020","unstructured":"Xiao B, Wei Y, Bi X, Li W, Ma J (2020) Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering. Inf Sci 511:172\u2013191","journal-title":"Inf Sci"},{"key":"6416_CR3","doi-asserted-by":"crossref","unstructured":"Fadl, S., Megahed, A., Han, Q., & Qiong, L. (2020). Frame duplication and shuffling forgery detection technique in surveillance videos based on temporal average and gray level co-occurrence matrix. Multimed Tools Appl 1\u201325","DOI":"10.1007\/s11042-019-08603-z"},{"issue":"1","key":"6416_CR4","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1049\/iet-bmt.2017.0089","volume":"7","author":"L Li","year":"2018","unstructured":"Li L, Correia PL, Hadid A (2018) Face recognition under spoofing attacks: countermeasures and research directions. IET Biometrics 7(1):3\u201314","journal-title":"IET Biometrics"},{"issue":"3","key":"6416_CR5","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1109\/TLT.2016.2524570","volume":"9","author":"M Kaiiali","year":"2016","unstructured":"Kaiiali M, Ozkaya A, Altun H, Haddad H, Alier M (2016) Designing a secure exam management system (SEMS) for M-learning environments. IEEE Trans Learn Technol 9(3):258\u2013271","journal-title":"IEEE Trans Learn Technol"},{"key":"6416_CR6","doi-asserted-by":"publisher","first-page":"13868","DOI":"10.1109\/ACCESS.2017.2729161","volume":"5","author":"SR Arashloo","year":"2017","unstructured":"Arashloo SR, Kittler J, Christmas W (2017) An anomaly detection approach to face spoofing detection: a new formulation and evaluation protocol. IEEE Access 5:13868\u201313882","journal-title":"IEEE Access"},{"key":"6416_CR7","doi-asserted-by":"crossref","unstructured":"Mohan K, Chandrasekhar P, Jilani SAK (2017) A combined histogram of oriented gradient-local phase quantization (HOG-LPQ) with fuzzy-support vector machine (Fuz-SVM) classifier for object face liveness detection. In: 2017 International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (pp. 531\u2013537). IEEE","DOI":"10.1109\/I-SMAC.2017.8058406"},{"key":"6416_CR8","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1016\/j.protcy.2016.05.118","volume":"24","author":"T Faseela","year":"2016","unstructured":"Faseela T, Jayasree M (2016) Spoof face recognition in video using KSVM. Proc Technol 24:1285\u20131291","journal-title":"Proc Technol"},{"key":"6416_CR9","doi-asserted-by":"crossref","unstructured":"Li L, Feng X, Boulkenafet Z, Xia Z, Li M, Hadid A (2016) An original face anti-spoofing approach using partial convolutional neural network. In: 2016 Sixth international conference on image processing theory, tools and applications (IPTA) (pp. 1\u20136). IEEE","DOI":"10.1109\/IPTA.2016.7821013"},{"issue":"1\u20132","key":"6416_CR10","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1007\/s11042-019-08115-w","volume":"79","author":"E Fourati","year":"2020","unstructured":"Fourati E, Elloumi W, Chetouani A (2020) Anti-spoofing in face recognition-based biometric authentication using image quality assessment. Multimed Tools Appl 79(1\u20132):865\u2013889","journal-title":"Multimed Tools Appl"},{"key":"6416_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-020-00875-8","author":"BR Katika","year":"2020","unstructured":"Katika BR, Karthik K (2020) Face anti-spoofing by identity masking using random walk patterns and outlier detection. Pattern Anal Appl. https:\/\/doi.org\/10.1007\/s10044-020-00875-8","journal-title":"Pattern Anal Appl"},{"key":"6416_CR12","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/978-3-030-32583-1_4","volume-title":"Deep biometrics","author":"W Elloumi","year":"2020","unstructured":"Elloumi W, Chetouani A, Charrada TB, Fourati E (2020) Anti-spoofing in face recognition: deep learning and image quality assessment-based approaches. In: Jiang R, Li C-T, Crookes D, Meng W, Rosenberger C (eds) Deep biometrics. Springer, Cham, pp 51\u201369"},{"key":"6416_CR13","doi-asserted-by":"crossref","unstructured":"Anjos A, & Marcel S (2011) Counter-measures to photo attacks in face recognition: a public database and a baseline. In: 2011 international joint conference on Biometrics (IJCB) (pp. 1\u20137). IEEE","DOI":"10.1109\/IJCB.2011.6117503"},{"issue":"2","key":"6416_CR14","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1109\/TIP.2013.2292332","volume":"23","author":"J Galbally","year":"2013","unstructured":"Galbally J, Marcel S, Fierrez J (2013) Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition. IEEE Trans Image Process 23(2):710\u2013724","journal-title":"IEEE Trans Image Process"},{"issue":"7","key":"6416_CR15","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1109\/TIFS.2014.2322255","volume":"9","author":"N Erdogmus","year":"2014","unstructured":"Erdogmus N, Marcel S (2014) Spoofing face recognition with 3D masks. IEEE Trans Inf Forensics Secur 9(7):1084\u20131097","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"10","key":"6416_CR16","doi-asserted-by":"publisher","first-page":"2268","DOI":"10.1109\/TIFS.2016.2578288","volume":"11","author":"K Patel","year":"2016","unstructured":"Patel K, Han H, Jain AK (2016) Secure face unlock: spoof detection on smartphones. IEEE Trans Inf Forensics Secur 11(10):2268\u20132283","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"6416_CR17","doi-asserted-by":"crossref","unstructured":"Liu S, Yang B, Yuen PC, Zhao G (2016) A 3D mask face anti-spoofing database with real world variations. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 100\u2013106).","DOI":"10.1109\/CVPRW.2016.193"},{"key":"6416_CR18","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/j.jvcir.2016.03.019","volume":"38","author":"L Feng","year":"2016","unstructured":"Feng L, Po LM, Li Y, Xu X, Yuan F, Cheung TCH, Cheung KW (2016) Integration of image quality and motion cues for face anti-spoofing: a neural network approach. J Vis Commun Image Represent 38:451\u2013460","journal-title":"J Vis Commun Image Represent"},{"key":"6416_CR19","doi-asserted-by":"crossref","unstructured":"Lucena O, Junior A, Moia V, Souza R, Valle E, Lotufo R (2017) Transfer learning using convolutional neural networks for face anti-spoofing. In: International conference image analysis and recognition. (pp. 27\u201334). Springer, Cham","DOI":"10.1007\/978-3-319-59876-5_4"},{"key":"6416_CR20","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/TIFS.2019.2916652","volume":"15","author":"A George","year":"2019","unstructured":"George A, Mostaani Z, Geissenbuhler D, Nikisins O, Anjos A, Marcel S (2019) Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Trans Inf Forensics Secur 15:42\u201355","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"6416_CR21","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1109\/TIFS.2019.2934867","volume":"15","author":"R Tolosana","year":"2019","unstructured":"Tolosana R, Gomez-Barrero M, Busch C, Ortega-Garcia J (2019) Biometric presentation attack detection: beyond the visible spectrum. IEEE Trans Inf Forensics Secur 15:1261\u20131275","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"6416_CR22","doi-asserted-by":"publisher","first-page":"23012","DOI":"10.1109\/ACCESS.2019.2899367","volume":"7","author":"U Scherhag","year":"2019","unstructured":"Scherhag U, Rathgeb C, Merkle J, Breithaupt R, Busch C (2019) Face recognition systems under morphing attacks: a survey. IEEE Access 7:23012\u201323026","journal-title":"IEEE Access"},{"key":"6416_CR23","doi-asserted-by":"crossref","unstructured":"Liu A, Wan J, Escalera S, Jair Escalante H, Tan Z, Yuan Q, Li SZ (2019). Multi-modal face anti-spoofing attack detection challenge at cvpr2019. In Proceedings of the IEEE Conference on computer vision and pattern recognition workshops (pp. 0\u20130).","DOI":"10.1109\/CVPRW.2019.00202"},{"key":"6416_CR24","doi-asserted-by":"crossref","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. arXiv preprint http:\/\/arxiv.org\/abs\/2001.00179","DOI":"10.1016\/j.inffus.2020.06.014"},{"key":"6416_CR25","doi-asserted-by":"publisher","first-page":"108655","DOI":"10.1016\/j.automatica.2019.108655","volume":"112","author":"YC Liu","year":"2020","unstructured":"Liu YC, Bianchin G, Pasqualetti F (2020) Secure trajectory planning against undetectable spoofing attacks. Automatica 112:108655","journal-title":"Automatica"},{"key":"6416_CR26","doi-asserted-by":"publisher","first-page":"5977","DOI":"10.1007\/s11042-020-08693-0","volume":"80","author":"AA Mahmoud","year":"2020","unstructured":"Mahmoud AA, El-Shafai W, Taha TE, El-Rabaie ESM, Zahran O, El-Fishawy AS, Abd El-Samie FE (2020) A statistical framework for breast tumor classification from ultrasonic images. Multimed Tools Appl 80:5977","journal-title":"Multimed Tools Appl"},{"key":"6416_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2018.12.005","author":"JC Whittington","year":"2019","unstructured":"Whittington JC, Bogacz R (2019) Theories of error back-propagation in the brain. Trends Cognit Sci. https:\/\/doi.org\/10.1016\/j.tics.2018.12.005","journal-title":"Trends Cognit Sci"},{"key":"6416_CR28","first-page":"1","volume":"22","author":"SAE El-Gindy","year":"2021","unstructured":"El-Gindy SAE, Hamad A, El-Shafai W, Khalaf AA, El-Dolil SM, Taha TE, Abd El-Samie FE (2021) Efficient communication and EEG signal classification in wavelet domain for epilepsy patients. J Ambient Intell Humaniz Comput 22:1\u201316","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"6416_CR29","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.patrec.2018.03.028","volume":"126","author":"RD Labati","year":"2019","unstructured":"Labati RD, Mu\u00f1oz E, Piuri V, Sassi R, Scotti F (2019) Deep-ECG: convolutional neural networks for ECG biometric recognition. Pattern Recogn Lett 126:78\u201385","journal-title":"Pattern Recogn Lett"},{"key":"6416_CR30","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.image.2018.11.011","volume":"71","author":"W Shan","year":"2019","unstructured":"Shan W, Yi Y, Huang R, Xie Y (2019) Robust contrast enhancement forensics based on convolutional neural networks. Signal Process Image Commun 71:138\u2013146","journal-title":"Signal Process Image Commun"},{"key":"6416_CR31","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1002\/jemt.23596","volume":"84","author":"NA El-Hag","year":"2020","unstructured":"El-Hag NA, Sedik A, El-Shafai W, El-Hoseny HM, Khalaf AA, El-Fishawy AS, El-Banby GM (2020) Classification of retinal images based on convolutional neural network. Microsc Res Tech 84:394","journal-title":"Microsc Res Tech"},{"key":"6416_CR32","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.neucom.2018.03.080","volume":"328","author":"Q Xu","year":"2019","unstructured":"Xu Q, Zhang M, Gu Z, Pan G (2019) Overfitting remedy by sparsifying regularization on fully-connected layers of CNNs. Neurocomputing 328:69\u201374","journal-title":"Neurocomputing"},{"issue":"7","key":"6416_CR33","doi-asserted-by":"publisher","first-page":"3286","DOI":"10.1109\/TIP.2019.2895466","volume":"28","author":"JH Bappy","year":"2019","unstructured":"Bappy JH, Simons C, Nataraj L, Manjunath BS, Roy-Chowdhury AK (2019) Hybrid lstm and encoder\u2013decoder architecture for detection of image forgeries. IEEE Trans Image Process 28(7):3286\u20133300","journal-title":"IEEE Trans Image Process"},{"key":"6416_CR34","unstructured":"Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst pp 3104\u20133112"},{"key":"6416_CR35","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.patcog.2018.07.034","volume":"85","author":"AK Bhunia","year":"2019","unstructured":"Bhunia AK, Konwer A, Bhunia AK, Bhowmick A, Roy PP, Pal U (2019) Script identification in natural scene image and video frames using an attention based convolutional-LSTM network. Pattern Recogn 85:172\u2013184","journal-title":"Pattern Recogn"},{"key":"6416_CR36","doi-asserted-by":"crossref","unstructured":"Bock S, Wei\u00df M (2019) A proof of local convergence for the Adam optimizer. In: 2019 International Joint Conference On Neural Networks (IJCNN) (pp. 1\u20138). IEEE.","DOI":"10.1109\/IJCNN.2019.8852239"},{"issue":"4","key":"6416_CR37","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1109\/TIFS.2015.2398819","volume":"10","author":"DF Smith","year":"2015","unstructured":"Smith DF, Wiliem A, Lovell BC (2015) Face recognition on consumer devices: reflections on replay attacks. IEEE Trans Inf Forensics Secur 10(4):736\u2013745","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"4","key":"6416_CR38","doi-asserted-by":"publisher","first-page":"1438","DOI":"10.1109\/TII.2016.2573259","volume":"12","author":"KT Chui","year":"2016","unstructured":"Chui KT, Tsang KF, Chi HR, Ling BWK, Wu CK (2016) An accurate ECG-based transportation safety drowsiness detection scheme. IEEE Trans Ind Inf 12(4):1438\u20131452","journal-title":"IEEE Trans Ind Inf"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06416-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06416-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06416-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T15:29:48Z","timestamp":1642778988000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06416-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,5]]},"references-count":38,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["6416"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06416-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,5]]},"assertion":[{"value":"16 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2021","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}