{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T14:52:38Z","timestamp":1780930358053,"version":"3.54.1"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-20223-w","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T06:02:08Z","timestamp":1727762528000},"page":"28603-28641","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MaD-CoRN: an efficient and lightweight deepfake detection approach using convolutional reservoir network"],"prefix":"10.1007","volume":"84","author":[{"given":"Rajat","family":"Budhiraja","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manish","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. K.","family":"Das","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anil Singh","family":"Bafila","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amit","family":"Pundir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2416-7011","authenticated-orcid":false,"given":"Sanjeev","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"20223_CR1","doi-asserted-by":"publisher","unstructured":"Ibsen M, Rathgeb C, Fischer D, Drozdowski P, Busch C (2022) Digital face manipulation in biometric systems. In: Rathgeb C, Tolosana R, Vera-Rodriguez R, Busch C (eds) Handbook of digital face manipulation and detection, advances in computer vision and pattern recognition. Springer, Cham pp 27\u201343. https:\/\/doi.org\/10.1007\/978-3-030-87664-7_2","DOI":"10.1007\/978-3-030-87664-7_2"},{"issue":"2","key":"20223_CR2","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1257\/jep.31.2.211","volume":"31","author":"H Allcott","year":"2017","unstructured":"Allcott H, Gentzkow M (2017) Social media and fake news in the 2016 election. J Econ Perspect 31(2):211\u2013236. https:\/\/doi.org\/10.1257\/jep.31.2.211","journal-title":"J Econ Perspect"},{"key":"20223_CR3","doi-asserted-by":"publisher","unstructured":"Liu Z, Luo P, Wang X, Tang X (2015) Deep learning face attributes in the wild. In: Proceedings of the IEEE international conference on computer vision: 3730\u20133738. https:\/\/doi.org\/10.1109\/ICCV.2015.425","DOI":"10.1109\/ICCV.2015.425"},{"key":"20223_CR4","unstructured":"Korshunov P, Marcel S (2018) DeepFakes: a new threat to face recognition? assessment and detection. arXiv:1812.08685. Accessed 2 Feb 2024"},{"key":"20223_CR5","doi-asserted-by":"publisher","unstructured":"Gaur L, Arora G, Jhanjhi N (2022) Deep learning techniques for creation of DeepFakes. In: Gaur L (ed) DeepFakes. 1st edn. CRC Press pp 23\u201334. https:\/\/doi.org\/10.1201\/9781003231493-3","DOI":"10.1201\/9781003231493-3"},{"key":"20223_CR6","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3761453","author":"R Gordon","year":"2021","unstructured":"Gordon R, Budish R (2021) Skin in the game: modulate AI and addressing the legal and ethical challenges of voice skin technology. SSRN Electron J. https:\/\/doi.org\/10.2139\/ssrn.3761453","journal-title":"SSRN Electron J"},{"key":"20223_CR7","unstructured":"Neekhara P, Hussain S, Dubnov S, Koushanfar F, McAuley J (2021) Expressive neural voice cloning. arXiv:2102.00151 . Accessed 2 Feb 2024"},{"key":"20223_CR8","unstructured":"Tong A, Ulmer A (2023) Deepfaking it: America\u2019s 2024 election collides with AI boom. https:\/\/www.reuters.com\/world\/us\/deepfaking-it-americas-2024- election-collides-with-ai-boom-2023-05-30\/. Accessed 2 May 2024"},{"key":"20223_CR9","unstructured":"Bateman J (2020) Deepfakes and synthetic media in the financial system: assessing threat scenarios. Carnegie endowment for international peace. https:\/\/www.jstor.org\/stable\/resrep25783"},{"key":"20223_CR10","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger KQ (eds) Advances in neural information processing systems. Curran Associates, 27:2672\u20132680"},{"key":"20223_CR11","doi-asserted-by":"publisher","unstructured":"Hinton GE, Krizhevsky A, Wang SD (2011) Transforming auto-encoders. In: Honkela T, Duch W, Girolami M, Kaski S (eds) Artificial neural networks and machine learning - ICANN 2011, 6791. Springer, Berlin, Heidelberg pp 44\u201351. https:\/\/doi.org\/10.1007\/978-3-642-21735-7_6","DOI":"10.1007\/978-3-642-21735-7_6"},{"key":"20223_CR12","unstructured":"Cirillo S, Desiato D, Scalera M, Solimando G (2023) A visual privacy tool to help users in preserving social network data. In: Joint proceedings of the workshops, work in progress demos and doctoral consortium at the IS-EUD 2023 co-located with the 9th international symposium on end-user development (IS-EUD 2023), 3408. CEUR, Cagliari, Italy"},{"key":"20223_CR13","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/s40537-022-00566-7","volume":"9","author":"F Cerruto","year":"2022","unstructured":"Cerruto F, Cirillo S, Desiato D, Gambardella SM, Polese G (2022) Social network data analysis to highlight privacy threats in sharing data. J Big Data 9:19. https:\/\/doi.org\/10.1186\/s40537-022-00566-7","journal-title":"J Big Data"},{"key":"20223_CR14","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 KM, Javed A, Irtaza A, Malik H (2023) Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward. Appl Intell 53:3974\u20134026. https:\/\/doi.org\/10.1007\/s10489-022-03766-z","journal-title":"Appl Intell"},{"key":"20223_CR15","doi-asserted-by":"publisher","unstructured":"Thies J, Zollhofer M, Stamminger M, Theobalt C, Niebner M (2016) Face2Face: real-time face capture and reenactment of RGB videos. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 2387\u20132395. https:\/\/doi.org\/10.1109\/CVPR.2016.262","DOI":"10.1109\/CVPR.2016.262"},{"key":"20223_CR16","doi-asserted-by":"publisher","unstructured":"Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition pp 4396\u20134405. https:\/\/doi.org\/10.1109\/CVPR.2019.00453","DOI":"10.1109\/CVPR.2019.00453"},{"issue":"1","key":"20223_CR17","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s11263-010-0403-1","volume":"92","author":"I Yerushalmy","year":"2011","unstructured":"Yerushalmy I, Hel-Or H (2011) Digital image forgery detection based on lens and sensor aberration. Int J Comput Vis 92(1):71\u201391. https:\/\/doi.org\/10.1007\/s11263-010-0403-1","journal-title":"Int J Comput Vis"},{"issue":"10","key":"20223_CR18","doi-asserted-by":"publisher","first-page":"3948","DOI":"10.1109\/TSP.2005.855406","volume":"53","author":"AC Popescu","year":"2005","unstructured":"Popescu AC, Farid H (2005) Exposing digital forgeries in color filter array interpolated images. IEEE Trans Signal Proc 53(10):3948\u20133959. https:\/\/doi.org\/10.1109\/TSP.2005.855406","journal-title":"IEEE Trans Signal Proc"},{"issue":"1","key":"20223_CR19","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/TIFS.2008.2012215","volume":"4","author":"H Farid","year":"2009","unstructured":"Farid H (2009) Exposing digital forgeries from jpeg ghosts. IEEE Trans Inf Forensics Secur 4(1):154\u2013160. https:\/\/doi.org\/10.1109\/TIFS.2008.2012215","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"2","key":"20223_CR20","doi-asserted-by":"publisher","first-page":"653","DOI":"10.12928\/TELKOMNIKA.V17I2.8976","volume":"17","author":"IBK Sudiatmika","year":"2019","unstructured":"Sudiatmika IBK, Rahman F, Trisno Suyoto (2019) Image forgery detection using error level analysis and deep learning. Telkomnika (Telecommunication Computing Electronics and Control) 17(2):653\u2013659. https:\/\/doi.org\/10.12928\/TELKOMNIKA.V17I2.8976","journal-title":"Telkomnika (Telecommunication Computing Electronics and Control)"},{"key":"20223_CR21","doi-asserted-by":"publisher","unstructured":"Nirkin Y, Wolf L, Keller Y, Hassner T (2021) DeepFake detection based on discrepancies between faces and their context. IEEE Trans Patt Anal Mach Intell 1\u20131. https:\/\/doi.org\/10.1109\/TPAMI.2021.3093446","DOI":"10.1109\/TPAMI.2021.3093446"},{"key":"20223_CR22","doi-asserted-by":"publisher","unstructured":"Khodabakhsh A, Ramachandra R, Raja K, Wasnik P, Busch C (2018) Fake face detection methods: can they be generalized?. In: International conference of the biometrics special interest group BIOSIG. https:\/\/doi.org\/10.23919\/BIOSIG.2018.8553251","DOI":"10.23919\/BIOSIG.2018.8553251"},{"key":"20223_CR23","unstructured":"Thompson NC, Greenewald K, Lee K, Manso GF (2020) The computational limits of deep learning. arXiv:2007.05558. Accessed 2 Feb 2024"},{"key":"20223_CR24","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"20223_CR25","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"20223_CR26","unstructured":"Jaeger H (2001) The \u201cecho state\u201d approach to analysing and training recurrent neural networks. GMD Report 148 German National Research Center for Information Technology"},{"key":"20223_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2009.03.005","author":"M Luko\u0161evi\u010dius","year":"2009","unstructured":"Luko\u0161evi\u010dius M, Jaeger H (2009) Reservoir computing approaches to recurrent neural network training. Comput Sci Rev. https:\/\/doi.org\/10.1016\/j.cosrev.2009.03.005","journal-title":"Comput Sci Rev"},{"issue":"1","key":"20223_CR28","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3(1):9. https:\/\/doi.org\/10.1186\/s40537-016-0043-6","journal-title":"J Big Data"},{"key":"20223_CR29","unstructured":"Real and Fake Face Detection Dataset (n.d.) Kaggle. https:\/\/www.kaggle.com\/datasets\/ciplab\/real-and-fake-face-detection. Accessed 2 Feb 2024"},{"key":"20223_CR30","doi-asserted-by":"publisher","first-page":"152667","DOI":"10.1109\/ACCESS.2019.2948526","volume":"7","author":"C Rathgeb","year":"2019","unstructured":"Rathgeb C, Dantcheva A, Busch C (2019) Impact and detection of facial beautification in face recognition: an overview. IEEE Access 7:152667\u2013152678. https:\/\/doi.org\/10.1109\/ACCESS.2019.2948526","journal-title":"IEEE Access"},{"key":"20223_CR31","doi-asserted-by":"publisher","unstructured":"Bharati A, Singh R, Vatsa M, Bowyer KW (2016) Detecting facial retouching using supervised deep learning. In: IEEE Trans Inf Forensics Secur 11(9), pp 1903\u20131913. https:\/\/doi.org\/10.1109\/TIFS.2016.2561898","DOI":"10.1109\/TIFS.2016.2561898"},{"key":"20223_CR32","doi-asserted-by":"publisher","unstructured":"Rathgeb C, Botaljov A, Stockhardt F, Isadskiy S, Debiasi L, Uhl A, Busch C (2020) PRNU-based detection of facial retouching. IET Biometrics 9(4):154\u2013164 ISSN 2047\u20134938. https:\/\/doi.org\/10.1049\/iet-bmt.2019.0196","DOI":"10.1049\/iet-bmt.2019.0196"},{"key":"20223_CR33","doi-asserted-by":"publisher","unstructured":"Lugstein F, Baier S, Bachinger G, Uhl A (2021) PRNU-based Deepfake detection. In: Proceedings of the 2021 ACM workshop on information hiding and multimedia security: 7\u201312. https:\/\/doi.org\/10.1145\/3437880.3460400","DOI":"10.1145\/3437880.3460400"},{"key":"20223_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-16329-2","author":"A Megahed","year":"2023","unstructured":"Megahed A, Han Q, Fadl S (2023) Exposing deepfake using fusion of deep-learned and hand-crafted features. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-023-16329-2","journal-title":"Multimed Tools Appl"},{"key":"20223_CR35","doi-asserted-by":"publisher","unstructured":"Huda N, ul, Javed A, Maswadi K, Alhazmi A, Ashraf R, (2023) Fake-checker: a fusion of texture features and deep learning for deepfakes detection. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-023-17586-x","DOI":"10.1007\/s11042-023-17586-x"},{"key":"20223_CR36","doi-asserted-by":"publisher","unstructured":"Dang H, Liu F, Stehouwer J, Liu X, Jain AK (2020) On the detection of digital face manipulation. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition 5780\u20135789. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00582","DOI":"10.1109\/CVPR42600.2020.00582"},{"key":"20223_CR37","doi-asserted-by":"crossref","unstructured":"Mccloskey S, Albright M, Acst H (2018) Detecting GAN-generated imagery using color cues. arXiv:1812.08247. Accessed 2 Feb 2024","DOI":"10.1109\/ICIP.2019.8803661"},{"key":"20223_CR38","doi-asserted-by":"publisher","unstructured":"Wang R, Juefei-Xu F, Ma L, Xie X, Huang Y, Wang J, Liu Y (2020) FakeSpotter: a simple yet robust baseline for spotting AI-synthesized fake faces. In: IJCAI International Joint Conference on Artificial Intelligence 3444\u20133451. https:\/\/doi.org\/10.24963\/ijcai.2020\/476","DOI":"10.24963\/ijcai.2020\/476"},{"key":"20223_CR39","doi-asserted-by":"publisher","unstructured":"Guarnera L, Giudice O, Battiato S (2020) DeepFake detection by analyzing convolutional traces. In: IEEE computer society conference on computer vision and pattern recognition workshops 2841\u20132850. https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00341","DOI":"10.1109\/CVPRW50498.2020.00341"},{"key":"20223_CR40","doi-asserted-by":"publisher","unstructured":"Usmani S, Kumar S, Sadhya D (2023) Efficient deepfake detection using shallow vision transformer. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-023-15910-z","DOI":"10.1007\/s11042-023-15910-z"},{"key":"20223_CR41","doi-asserted-by":"publisher","unstructured":"LeCun Y, Kavukcuoglu K, Farabet C (2010) Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE international symposium on circuits and systems 253\u2013256. https:\/\/doi.org\/10.1109\/ISCAS.2010.5537907","DOI":"10.1109\/ISCAS.2010.5537907"},{"key":"20223_CR42","doi-asserted-by":"publisher","unstructured":"Nataraj L, Mohammed TM, Manjunath BS, Chandrasekaran S, Flenner A, Bappy JH, Roy-Chowdhury AK (2019) Detecting GAN generated fake images using cooccurrence matrices. In: IS and T International Symposium on Electronic Imaging Science and Technology 2019(5). https:\/\/doi.org\/10.2352\/ISSN.2470-1173.2019.5.MWSF-532","DOI":"10.2352\/ISSN.2470-1173.2019.5.MWSF-532"},{"key":"20223_CR43","doi-asserted-by":"publisher","unstructured":"Marra F, Saltori C, Boato G, Verdoliva L (2019) Incremental learning for the detection and classification of GAN-generated images. In: 2019 IEEE international workshop on information forensics and security. https:\/\/doi.org\/10.1109\/WIFS47025.2019.9035099","DOI":"10.1109\/WIFS47025.2019.9035099"},{"key":"20223_CR44","first-page":"1097","volume":"2","author":"A Krizhevsk","year":"2012","unstructured":"Krizhevsk A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"20223_CR45","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations ICLR 2015-conference track proceedings"},{"key":"20223_CR46","doi-asserted-by":"publisher","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition: 2818\u20132826. https:\/\/doi.org\/10.1109\/CVPR.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"20223_CR47","doi-asserted-by":"publisher","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of 30th IEEE conference on computer vision and pattern recognition: 1800\u20131807. https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"20223_CR48","doi-asserted-by":"publisher","unstructured":"Tariq S, Lee S, Kim H, Shin Y, Woo SS (2018) Detecting both machine and human created fake face images in the wild. In: Proceedings of the ACM conference on computer and communications security 81\u201387. https:\/\/doi.org\/10.1145\/3267357.3267367","DOI":"10.1145\/3267357.3267367"},{"key":"20223_CR49","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1109\/SSCI47803.2020.9308337","volume":"2020","author":"H Mittal","year":"2020","unstructured":"Mittal H, Saraswat M, Bansal JC, Nagar A (2020) Fake-face image classification using improved quantum-inspired evolutionary-based feature selection method. IEEE Symp Ser Comput Intell SSCI 2020:989\u2013995. https:\/\/doi.org\/10.1109\/SSCI47803.2020.9308337","journal-title":"IEEE Symp Ser Comput Intell SSCI"},{"key":"20223_CR50","doi-asserted-by":"publisher","unstructured":"Qurat-Ul-Ain, Nida N, Irtaza A, Ilyas N (2021) Forged face detection using ELA and deep learning techniques. In: Proceedings of 18th international Bhurban conference on applied sciences and technologies, pp 271\u2013275. https:\/\/doi.org\/10.1109\/IBCAST51254.2021.9393234","DOI":"10.1109\/IBCAST51254.2021.9393234"},{"key":"20223_CR51","doi-asserted-by":"publisher","first-page":"4234","DOI":"10.1109\/TIFS.2021.3102487","volume":"16","author":"J Yang","year":"2021","unstructured":"Yang J, Li A, Xiao S, Lu W, Gao X (2021) MTD-Net: learning to detect deepfakes images by multi-scale texture difference. IEEE Trans Inf Forensics Secur 16:4234\u20134245. https:\/\/doi.org\/10.1109\/TIFS.2021.3102487","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"7","key":"20223_CR52","doi-asserted-by":"publisher","first-page":"4854","DOI":"10.1109\/TCSVT.2021.3133859","volume":"32","author":"J Yang","year":"2022","unstructured":"Yang J, Xiao S, Li A, Lu W, Gao X, Li Y (2022) MSTA-Net: forgery detection by generating manipulation trace based on multi-scale self-texture attention. IEEE Trans Circ Sys Video Tech 32(7):4854\u20134866. https:\/\/doi.org\/10.1109\/TCSVT.2021.3133859","journal-title":"IEEE Trans Circ Sys Video Tech"},{"key":"20223_CR53","doi-asserted-by":"publisher","unstructured":"Singh J, Ramachandra R (2022) DLDFD: recurrence free 2D convolution approach for deep fake detection. In: Proceedings of the 17th international joint conference on computer vision imaging and computer graphics theory and applications 4 ISBN 978-989-758-555-5, pp 568\u2013574. https:\/\/doi.org\/10.5220\/0010880500003124","DOI":"10.5220\/0010880500003124"},{"key":"20223_CR54","doi-asserted-by":"publisher","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 105. https:\/\/doi.org\/10.1016\/j.asoc.2021.107256","DOI":"10.1016\/j.asoc.2021.107256"},{"issue":"9","key":"20223_CR55","doi-asserted-by":"publisher","first-page":"519","DOI":"10.22214\/ijraset.2022.45829","volume":"10","author":"P Patil","year":"2022","unstructured":"Patil P, Deshpande V, Malge V, Bevinmanchi A (2022) Fake face detection using CNN. Int J Res Appl Sci Eng Technol 10(9):519\u2013522. https:\/\/doi.org\/10.22214\/ijraset.2022.45829","journal-title":"Int J Res Appl Sci Eng Technol"},{"issue":"2","key":"20223_CR56","doi-asserted-by":"publisher","first-page":"e0246737","DOI":"10.1371\/journal.pone.0246737","volume":"16","author":"R Budhiraja","year":"2021","unstructured":"Budhiraja R, Kumar M, Das MK, Bafila AS, Singh S (2021) A reservoir computing approach for forecasting and regenerating both dynamical and time-delay controlled financial system behavior. PLoS ONE 16(2):e0246737. https:\/\/doi.org\/10.1371\/journal.pone.0246737","journal-title":"PLoS ONE"},{"key":"20223_CR57","unstructured":"Bianchi FM, Scardapane S, L\u00f8kse S, Jenssen R (2018) Bidirectional deep-readout echo state networks. In: ESANN 2018 - proceedings, European symposium on artificial neural networks, computational intelligence and machine learning 425\u2013430"},{"key":"20223_CR58","doi-asserted-by":"publisher","unstructured":"Verstraeten D, Schrauwen B, Stroobandt D (2006) Reservoir-based techniques for speech recognition. In: IEEE international conference on neural networks - conference proceedings. https:\/\/doi.org\/10.1109\/ijcnn.2006.246804","DOI":"10.1109\/ijcnn.2006.246804"},{"key":"20223_CR59","doi-asserted-by":"publisher","unstructured":"Tong Z, Tanaka G (2018) Reservoir computing with untrained convolutional neural networks for image recognition. In: 24th International Conference on Pattern Recognition (ICPR) 1289\u20131294. https:\/\/doi.org\/10.1109\/ICPR.2018.8545471","DOI":"10.1109\/ICPR.2018.8545471"},{"key":"20223_CR60","doi-asserted-by":"publisher","unstructured":"Bhovad P, Li S (2021) Physical reservoir computing with origami and its application to robotic crawling. Scientific Reports 11(1). https:\/\/doi.org\/10.1038\/s41598-021-92257-1","DOI":"10.1038\/s41598-021-92257-1"},{"key":"20223_CR61","doi-asserted-by":"publisher","DOI":"10.1126\/science.1091277","author":"H Jaeger","year":"2004","unstructured":"Jaeger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science. https:\/\/doi.org\/10.1126\/science.1091277","journal-title":"Science"},{"issue":"5","key":"20223_CR62","doi-asserted-by":"publisher","first-page":"2169","DOI":"10.1109\/TNNLS.2020.3001377","volume":"32","author":"FM Bianchi","year":"2021","unstructured":"Bianchi FM, Scardapane S, Lokse S, Jenssen R (2021) Reservoir computing approaches for representation and classification of multivariate time series. IEEE Trans Neural Net Learn Sys 32(5):2169\u20132179. https:\/\/doi.org\/10.1109\/TNNLS.2020.3001377","journal-title":"IEEE Trans Neural Net Learn Sys"},{"key":"20223_CR63","doi-asserted-by":"publisher","unstructured":"Lu Z, Pathak J, Hunt B, Girvan M, Brockett R, Ott E (2017) Reservoir observers: model-free inference of unmeasured variables in chaotic systems. Chaos. https:\/\/doi.org\/10.1063\/1.4979665","DOI":"10.1063\/1.4979665"},{"key":"20223_CR64","unstructured":"Jaeger H (2013) A tutorial on training recurrent neural networks , covering BPPT , RTRL , EKF and the \u201cecho state network\u201d approach. ReVision"},{"issue":"3","key":"20223_CR65","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s12559-017-9468-2","volume":"9","author":"AJ Wootton","year":"2017","unstructured":"Wootton AJ, Taylor SL, Day CR, Haycock PW (2017) Optimizing echo state networks for static pattern recognition. Cogn Comput 9(3):391\u2013399. https:\/\/doi.org\/10.1007\/s12559-017-9468-2","journal-title":"Cogn Comput"},{"key":"20223_CR66","doi-asserted-by":"publisher","unstructured":"Meftah B, L$$e^{^{\\prime }}$$zoray O, Benyettou A, (2016) Novel approach using echo state networks for microscopic cellular image segmentation. Cogn Comput 8(2):237\u2013245. https:\/\/doi.org\/10.1007\/s12559-015-9354-8","DOI":"10.1007\/s12559-015-9354-8"},{"key":"20223_CR67","doi-asserted-by":"publisher","unstructured":"Kitayama M, Kiya H (2019) HOG feature extraction from encrypted images for privacy-preserving machine learning. In: IEEE international conference on consumer electronics pp 80\u201382. https:\/\/doi.org\/10.1109\/ICCE-Asia46551.2019.8942217","DOI":"10.1109\/ICCE-Asia46551.2019.8942217"},{"issue":"24","key":"20223_CR68","doi-asserted-by":"publisher","first-page":"25851","DOI":"10.1007\/s11042-017-5189-5","volume":"76","author":"MM Isaac","year":"2017","unstructured":"Isaac MM, Wilscy M (2017) Multiscale local Gabor phase quantization for image forgery detection. Multimed Tools Appl 76(24):25851\u201325872. https:\/\/doi.org\/10.1007\/s11042-017-5189-5","journal-title":"Multimed Tools Appl"},{"key":"20223_CR69","doi-asserted-by":"publisher","unstructured":"Zhang W, Zhao C, Li Y (2020) A novel counterfeit feature extraction technique for exposing face-swap images based on deep learning and error level analysis. Entropy 22(2). https:\/\/doi.org\/10.3390\/e22020249","DOI":"10.3390\/e22020249"},{"key":"20223_CR70","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.jvcir.2018.05.009","volume":"54","author":"L Li","year":"2018","unstructured":"Li L, Feng X, Xia Z, Jiang X, Hadid A (2018) Face spoofing detection with local binary pattern network. J Vis Comm Image Repr 54:182\u2013192. https:\/\/doi.org\/10.1016\/j.jvcir.2018.05.009","journal-title":"J Vis Comm Image Repr"},{"key":"20223_CR71","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-024-03354-x","author":"L Wang","year":"2024","unstructured":"Wang L, Li J, Guo S, Han S (2024) A cascaded graph convolutional network for point cloud completion. Vis Comput. https:\/\/doi.org\/10.1007\/s00371-024-03354-x","journal-title":"Vis Comput"},{"key":"20223_CR72","doi-asserted-by":"publisher","first-page":"2931","DOI":"10.1007\/s11280-023-01149-z","volume":"26","author":"F Li","year":"2023","unstructured":"Li F, Wang X, Sun Y, Li T, Ge J (2023) Transfer learning based cascaded deep learning network and mask recognition for COVID-19. World Wide Web 26:2931\u20132946. https:\/\/doi.org\/10.1007\/s11280-023-01149-z","journal-title":"World Wide Web"},{"key":"20223_CR73","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s13278-024-01231-y","volume":"14","author":"H Aouani","year":"2024","unstructured":"Aouani H, Ben Ayed Y (2024) Deep facial expression detection using Viola-Jones algorithm, CNN-MLP and CNN-SVM. Soc Netw Anal Min 14:65. https:\/\/doi.org\/10.1007\/s13278-024-01231-y","journal-title":"Soc Netw Anal Min"},{"issue":"8","key":"20223_CR74","doi-asserted-by":"publisher","first-page":"2400","DOI":"10.1007\/s10489-020-01679-3","volume":"50","author":"H Chang","year":"2020","unstructured":"Chang H, Futagami K (2020) Reinforcement learning with convolutional reservoir computing. Appl Intell 50(8):2400\u20132410. https:\/\/doi.org\/10.1007\/s10489-020-01679-3","journal-title":"Appl Intell"},{"issue":"10","key":"20223_CR75","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499\u20131503. https:\/\/doi.org\/10.1109\/LSP.2016.2603342","journal-title":"IEEE Signal Process Lett"},{"key":"20223_CR76","doi-asserted-by":"publisher","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition 779\u2013788. https:\/\/doi.org\/10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20223-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-20223-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20223-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T10:18:01Z","timestamp":1753265881000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-20223-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,1]]},"references-count":76,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["20223"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-20223-w","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,1]]},"assertion":[{"value":"4 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2024","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 declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}}]}}