{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T16:44:29Z","timestamp":1786812269773,"version":"build-2736575974"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T00:00:00Z","timestamp":1676937600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T00:00:00Z","timestamp":1676937600000},"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"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s11042-023-14508-9","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T20:38:17Z","timestamp":1677011897000},"page":"28713-28738","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Unethical human action recognition using deep learning based hybrid model for video forensics"],"prefix":"10.1007","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4440-4683","authenticated-orcid":false,"given":"Raghavendra","family":"Gowada","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Digambar","family":"Pawar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biplab","family":"Barman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"issue":"5","key":"14508_CR1","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/j.cviu.2012.09.007","volume":"117","author":"S Avila","year":"2013","unstructured":"Avila S, Thome N, Cord M et al (2013) Pooling in image representation: the visual codeword point of view. Comput Vis Image Underst 117(5):453\u2013465","journal-title":"Comput Vis Image Underst"},{"key":"14508_CR2","doi-asserted-by":"crossref","unstructured":"Battiato S, Giudice O, Paratore A (2016) Multimedia forensics: discovering the history of multimedia contents. In: Proceedings of the 17th international conference on computer systems and technologies 2016, pp 5\u201316","DOI":"10.1145\/2983468.2983470"},{"key":"14508_CR3","doi-asserted-by":"crossref","unstructured":"Carreira J, Zisserman A (2017) Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6299\u20136308","DOI":"10.1109\/CVPR.2017.502"},{"key":"14508_CR4","doi-asserted-by":"crossref","unstructured":"Donahue J, Anne Hendricks L, Guadarrama S et al (2015) Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2625\u20132634","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"14508_CR5","unstructured":"Dumoulin V, Visin F (2016) A guide to convolution arithmetic for deep learning. arXiv:160307285"},{"key":"14508_CR6","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Pinz A, Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1933\u20131941","DOI":"10.1109\/CVPR.2016.213"},{"issue":"12","key":"14508_CR7","doi-asserted-by":"publisher","first-page":"2247","DOI":"10.1109\/TPAMI.2007.70711","volume":"29","author":"L Gorelick","year":"2007","unstructured":"Gorelick L, Blank M, Shechtman E et al (2007) Actions as space-time shapes. IEEE Trans Pattern Anal Mach Intell 29(12):2247\u20132253","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"14508_CR8","doi-asserted-by":"publisher","first-page":"45,753","DOI":"10.1109\/ACCESS.2020.2978223","volume":"8","author":"Y Huang","year":"2020","unstructured":"Huang Y, Guo Y, Gao C (2020) Efficient parallel inflated 3d convolution architecture for action recognition. IEEE Access 8:45,753\u201345,765","journal-title":"IEEE Access"},{"issue":"1","key":"14508_CR9","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/s42835-018-00012-w","volume":"14","author":"A Jalal","year":"2019","unstructured":"Jalal A, Kamal S, Azurdia-Meza CA (2019) Depth maps-based human segmentation and action recognition using full-body plus body color cues via recognizer engine. Journal of Electrical Engineering & Technology 14(1):455\u2013461","journal-title":"Journal of Electrical Engineering & Technology"},{"issue":"1","key":"14508_CR10","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2012","unstructured":"Ji S, Xu W, Yang M et al (2012) 3d convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221\u2013231","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"14508_CR11","unstructured":"Karnataka Minister involved in SEX CD scandal (2021) IndiaToday. https:\/\/bit.ly\/37I8ZCV, [Online; accessed 23-March-2021]"},{"key":"14508_CR12","doi-asserted-by":"crossref","unstructured":"Karpathy A, Toderici G, Shetty S et al (2014) Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1725\u20131732","DOI":"10.1109\/CVPR.2014.223"},{"key":"14508_CR13","unstructured":"Kay W, Carreira J, Simonyan K et al (2017) The kinetics human action video dataset. arXiv:170506950"},{"key":"14508_CR14","doi-asserted-by":"crossref","unstructured":"Khan MA, Javed K, Khan SA et al (2020) Human action recognition using fusion of multiview and deep features: an application to video surveillance. Multimed Tools Appl, pp 1\u201327","DOI":"10.1007\/s11042-020-08806-9"},{"key":"14508_CR15","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, vol 25"},{"key":"14508_CR16","doi-asserted-by":"crossref","unstructured":"Kuehne H, Jhuang H, Garrote E et al (2011) Hmdb: a large video database for human motion recognition. In: 2011 International conference on computer vision, IEEE, pp 2556\u20132563","DOI":"10.1109\/ICCV.2011.6126543"},{"issue":"11","key":"14508_CR17","doi-asserted-by":"publisher","first-page":"2990","DOI":"10.1109\/TMM.2020.2965434","volume":"22","author":"J Li","year":"2020","unstructured":"Li J, Liu X, Zhang W et al (2020) Spatio-temporal attention networks for action recognition and detection. IEEE Trans Multimedia 22(11):2990\u20133001","journal-title":"IEEE Trans Multimedia"},{"issue":"11","key":"14508_CR18","doi-asserted-by":"publisher","first-page":"301","DOI":"10.3390\/a13110301","volume":"13","author":"G Liu","year":"2020","unstructured":"Liu G, Zhang C, Xu Q et al (2020) I3d-shufflenet based human action recognition. Algorithms 13(11):301","journal-title":"Algorithms"},{"key":"14508_CR19","doi-asserted-by":"crossref","unstructured":"Liu J, Shahroudy A, Xu D, et al (2016) Spatio-temporal lstm with trust gates for 3d human action recognition. In: European conference on computer vision. Springer, pp 816\u2013833","DOI":"10.1007\/978-3-319-46487-9_50"},{"issue":"12","key":"14508_CR20","doi-asserted-by":"publisher","first-page":"18,693","DOI":"10.1007\/s11042-021-10570-3","volume":"80","author":"R Maqsood","year":"2021","unstructured":"Maqsood R, Bajwa UI, Saleem G et al (2021) Anomaly recognition from surveillance videos using 3d convolution neural network. Multimedia Tools and Applications 80(12):18,693\u201318,716","journal-title":"Multimedia Tools and Applications"},{"key":"14508_CR21","unstructured":"Moustafa M (2015) Applying deep learning to classify pornographic images and videos. arXiv:151108899"},{"key":"14508_CR22","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1016\/j.procs.2019.11.147","volume":"161","author":"SM Sam","year":"2019","unstructured":"Sam SM, Kamardin K, Sjarif NNA et al (2019) Offline signature verification using deep learning convolutional neural network (cnn) architectures googlenet inception-v1 and inception-v3. Procedia Computer Science 161:475\u2013483","journal-title":"Procedia Computer Science"},{"key":"14508_CR23","doi-asserted-by":"crossref","unstructured":"Sargano AB, Wang X, Angelov P et al (2017) Human action recognition using transfer learning with deep representations. In: 2017 international joint conference on neural networks (IJCNN). IEEE, pp 463-469","DOI":"10.1109\/IJCNN.2017.7965890"},{"key":"14508_CR24","doi-asserted-by":"crossref","unstructured":"Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local svm approach. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004., IEEE, pp 32\u201336","DOI":"10.1109\/ICPR.2004.1334462"},{"issue":"12","key":"14508_CR25","first-page":"310","volume":"6","author":"S Sharma","year":"2017","unstructured":"Sharma S, Sharma S, Athaiya A (2017) Activation functions in neural networks. Towards Data Science 6(12):310\u2013316","journal-title":"Towards Data Science"},{"key":"14508_CR26","doi-asserted-by":"crossref","unstructured":"Silva MVd, Marana AN (2018) Spatiotemporal cnns for pornography detection in videos. In: Iberoamerican congress on pattern recognition. Springer, pp 547\u2013555","DOI":"10.1007\/978-3-030-13469-3_64"},{"key":"14508_CR27","unstructured":"Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. Advances in Neural Information Processing Systems, vol 27"},{"key":"14508_CR28","unstructured":"Soomro K, Zamir AR, Shah M (2012) Ucf101: a dataset of 101 human actions classes from videos in the wild. arXiv:12120402"},{"key":"14508_CR29","doi-asserted-by":"crossref","unstructured":"Sultani W, Chen C, Shah M (2018) Real-world anomaly detection in surveillance videos. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6479\u20136488","DOI":"10.1109\/CVPR.2018.00678"},{"key":"14508_CR30","doi-asserted-by":"crossref","unstructured":"Tran D, Bourdev L, Fergus R, et al (2015) Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 4489\u20134497","DOI":"10.1109\/ICCV.2015.510"},{"issue":"6","key":"14508_CR31","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1109\/TPAMI.2017.2712608","volume":"40","author":"G Varol","year":"2017","unstructured":"Varol G, Laptev I, Schmid C (2017) Long-term temporal convolutions for action recognition. IEEE Trans Pattern Anal Mach Intell 40(6):1510\u20131517","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"14508_CR32","doi-asserted-by":"crossref","unstructured":"Wang X, Miao Z, Zhang R et al (2019) I3d-lstm: a new model for human action recognition. In: IOP conference series: materials science and engineering, IOP Publishing, pp 032035","DOI":"10.1088\/1757-899X\/569\/3\/032035"},{"key":"14508_CR33","doi-asserted-by":"crossref","unstructured":"Zhou Y, Sun X, Zha ZJ et al (2018) Mict: mixed 3d\/2d convolutional tube for human action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 449\u2013458","DOI":"10.1109\/CVPR.2018.00054"},{"key":"14508_CR34","unstructured":"Zhu Y, Newsam S (2019) Motion-aware feature for improved video anomaly detection. arXiv:190710211"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14508-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-14508-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14508-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T10:27:40Z","timestamp":1690021660000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-14508-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,21]]},"references-count":34,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["14508"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-14508-9","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,21]]},"assertion":[{"value":"16 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There are no conflict of interest\/Competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}