{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T06:00:10Z","timestamp":1790834410571,"version":"4.1.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T00:00:00Z","timestamp":1593993600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T00:00:00Z","timestamp":1593993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"The open fund of Engineering Research Center of Cognitive Healthcare of Zhejiang Province","award":["2018KFJJ05"],"award-info":[{"award-number":["2018KFJJ05"]}]},{"name":"Key Technology Research and Development Program of Zhejiang","award":["2019C03127"],"award-info":[{"award-number":["2019C03127"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602140"],"award-info":[{"award-number":["61602140"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702517"],"award-info":[{"award-number":["61702517"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972121"],"award-info":[{"award-number":["61972121"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1007\/s13042-020-01157-9","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T22:02:17Z","timestamp":1594072937000},"page":"73-86","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Fine-grained pornographic image recognition with multiple feature fusion transfer learning"],"prefix":"10.1007","volume":"12","author":[{"given":"Xinnan","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5036-9365","authenticated-orcid":false,"given":"Feiwei","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanli","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,7,6]]},"reference":[{"issue":"1","key":"1157_CR1","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1089\/cyber.2010.0477","volume":"15","author":"MB Short","year":"2012","unstructured":"Short MB, Black L, Smith AH, Wetterneck CT, Wells DE (2012) A review of internet pornography use research: methodology and content from the past 10 years. Cyberpsychol Behav Soc Netw 15(1):13\u201323. https:\/\/doi.org\/10.1089\/cyber.2010.0477","journal-title":"Cyberpsychol Behav Soc Netw"},{"issue":"1\u20132","key":"1157_CR2","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1080\/10720162.2012.660431","volume":"19","author":"EW Owens","year":"2012","unstructured":"Owens EW, Behun RJ, Manning JC, Reid RC (2012) The impact of internet pornography on adolescents: a review of the research. Sex Addict Compuls 19(1\u20132):99\u2013122. https:\/\/doi.org\/10.1080\/10720162.2012.660431","journal-title":"Sex Addict Compuls"},{"issue":"2\u20133","key":"1157_CR3","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1080\/10720160600870711","volume":"13","author":"JC Manning","year":"2006","unstructured":"Manning JC (2006) The impact of internet pornography on marriage and the family: a review of the research. Sex Addict Compuls 13(2\u20133):131\u2013165. https:\/\/doi.org\/10.1080\/10720160600870711","journal-title":"Sex Addict Compuls"},{"issue":"04","key":"1157_CR4","doi-asserted-by":"publisher","first-page":"1350012","DOI":"10.1142\/S0218001413500122","volume":"27","author":"A Zaidan","year":"2013","unstructured":"Zaidan A, Karim HA, Ahmad N, Zaidan B, Sali A (2013) An automated anti-pornography system using a skin detector based on artificial intelligence: a review. Int J Pattern Recognit Artif Intell 27(04):1350012. https:\/\/doi.org\/10.1142\/S0218001413500122","journal-title":"Int J Pattern Recognit Artif Intell"},{"key":"1157_CR5","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"key":"1157_CR6","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"1157_CR7","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1157_CR8","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"1157_CR9","doi-asserted-by":"publisher","unstructured":"Wang X, Cheng F, Wang S, Sun H, Liu G, Zhou C (2018) Adult image classification by a local-context aware network. In: 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE, pp 2989\u20132993, https:\/\/doi.org\/10.1109\/ICIP.2018.8451366","DOI":"10.1109\/ICIP.2018.8451366"},{"key":"1157_CR10","doi-asserted-by":"publisher","unstructured":"Zhu R, Wu X, Zhu B, Song L (2018) Application of pornographic images recognition based on depth learning. In: Proceedings of the 2018 International Conference on Information Science and System, ACM, pp 152\u2013155, https:\/\/doi.org\/10.1145\/3209914.3209946","DOI":"10.1145\/3209914.3209946"},{"key":"1157_CR11","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.neucom.2015.09.135","volume":"210","author":"F Nian","year":"2016","unstructured":"Nian F, Li T, Wang Y, Xu M, Wu J (2016) Pornographic image detection utilizing deep convolutional neural networks. Neurocomputing 210:283\u2013293. https:\/\/doi.org\/10.1016\/j.neucom.2015.09.135","journal-title":"Neurocomputing"},{"key":"1157_CR12","unstructured":"Moustafa M (2015) Applying deep learning to classify pornographic images and videos. arXiv preprint arXiv:151108899"},{"key":"1157_CR13","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.jvcir.2017.12.005","volume":"50","author":"P Vitorino","year":"2018","unstructured":"Vitorino P, Avila S, Perez M, Rocha A (2018) Leveraging deep neural networks to fight child pornography in the age of social media. J Vis Commun Image Represent 50:303\u2013313. https:\/\/doi.org\/10.1016\/j.jvcir.2017.12.005","journal-title":"J Vis Commun Image Represent"},{"key":"1157_CR14","doi-asserted-by":"publisher","unstructured":"Zhu H, Zhou S, Wang J, Yin Z (2007) An algorithm of pornographic image detection. In: Fourth International Conference on Image and Graphics (ICIG 2007), IEEE, pp 801\u2013804, https:\/\/doi.org\/10.1109\/ICIG.2007.29","DOI":"10.1109\/ICIG.2007.29"},{"issue":"1","key":"1157_CR15","doi-asserted-by":"publisher","first-page":"95","DOI":"10.7903\/ijecs.1408","volume":"7","author":"C Srisaan","year":"2016","unstructured":"Srisaan C (2016) A classification of internet pornographic images. Int J Electron Commerce Stud 7(1):95\u2013104. https:\/\/doi.org\/10.7903\/ijecs.1408","journal-title":"Int J Electron Commerce Stud"},{"key":"1157_CR16","doi-asserted-by":"publisher","unstructured":"Moreira DC, Fechine JM (2018) A machine learning-based forensic discriminator of pornographic and bikini images. In: 2018 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 1\u20138, https:\/\/doi.org\/10.1109\/IJCNN.2018.8489100","DOI":"10.1109\/IJCNN.2018.8489100"},{"key":"1157_CR17","doi-asserted-by":"publisher","unstructured":"Deselaers T, Pimenidis L, Ney H (2008) Bag-of-visual-words models for adult image classification and filtering. In: 2008 19th International Conference on pattern recognition, IEEE, pp 1\u20134, https:\/\/doi.org\/10.1109\/ICPR.2008.4761366","DOI":"10.1109\/ICPR.2008.4761366"},{"issue":"5","key":"1157_CR18","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, Valle E, Ara\u00faJo ADA (2013) Pooling in image representation: the visual codeword point of view. Comput Vis Image Underst 117(5):453\u2013465. https:\/\/doi.org\/10.1016\/j.cviu.2012.09.007","journal-title":"Comput Vis Image Underst"},{"key":"1157_CR19","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.neucom.2015.06.055","volume":"173","author":"L Zhuo","year":"2016","unstructured":"Zhuo L, Geng Z, Zhang J, Guang Li X (2016) ORB feature based web pornographic image recognition. Neurocomputing 173:511\u2013517. https:\/\/doi.org\/10.1016\/j.neucom.2015.06.055","journal-title":"Neurocomputing"},{"key":"1157_CR20","doi-asserted-by":"crossref","unstructured":"Liu Y, Gu X, Huang L, Ouyang J, Liao M, Wu L (2019) Analyzing periodicity and saliency for adult video detection. arXiv preprint arXiv:190103462","DOI":"10.1109\/BigMM.2018.8499467"},{"key":"1157_CR21","doi-asserted-by":"publisher","unstructured":"Tang S, Li J, Zhang Y, Xie C, Li M, Liu Y, Hua X, Zheng YT, Tang J, Chua TS (2009) Pornprobe: an lda-svm based pornography detection system. In: Proceedings of the 17th ACM International Conference on Multimedia, ACM, pp 1003\u20131004, https:\/\/doi.org\/10.1145\/1631272.1631490","DOI":"10.1145\/1631272.1631490"},{"key":"1157_CR22","doi-asserted-by":"publisher","unstructured":"Liu Y, Xie H (2009) Constructing surf visual-words for pornographic images detection. In: 2009 12th International Conference on computers and information technology, IEEE, pp 404\u2013407, https:\/\/doi.org\/10.1109\/ICCIT.2009.5407272","DOI":"10.1109\/ICCIT.2009.5407272"},{"key":"1157_CR23","doi-asserted-by":"publisher","unstructured":"Yizhi L, Shouxun L, Sheng T, Yongdong Z (2010) Adult image detection combining bovw based on region of interest and color moments. In: International Conference on intelligent information processing, Springer, pp 316\u2013325, https:\/\/doi.org\/10.1007\/978-3-642-16327-2_38","DOI":"10.1007\/978-3-642-16327-2_38"},{"key":"1157_CR24","doi-asserted-by":"publisher","first-page":"28936","DOI":"10.1109\/ACCESS.2018.2837654","volume":"6","author":"D Zhang","year":"2018","unstructured":"Zhang D, Zou L, Zhou X, He F (2018) Integrating feature selection and feature extraction methods with deep learning to predict clinical outcome of breast cancer. IEEE Access 6:28936\u201328944. https:\/\/doi.org\/10.1109\/ACCESS.2018.2837654","journal-title":"IEEE Access"},{"key":"1157_CR25","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556"},{"key":"1157_CR26","doi-asserted-by":"crossref","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 Conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1157_CR27","unstructured":"Kim A (2019) NSFW dataset. https:\/\/github.com\/alexkimxyz\/nsfw_data_scraper. Accessed 1 Apr 2019"},{"key":"1157_CR28","doi-asserted-by":"crossref","unstructured":"Oquab M, Bottou L, Laptev I, Sivic J (2014) Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 1717\u20131724","DOI":"10.1109\/CVPR.2014.222"},{"key":"1157_CR29","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:150203167"},{"key":"1157_CR30","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the fourteenth International Conference on artificial intelligence and statistics, pp 315\u2013323"},{"issue":"10","key":"1157_CR31","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359. https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1157_CR32","doi-asserted-by":"publisher","unstructured":"Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C (2018) A survey on deep transfer learning. In: International Conference on artificial neural networks, Springer, pp 270\u2013279, https:\/\/doi.org\/10.1007\/978-3-030-01424-7_27","DOI":"10.1007\/978-3-030-01424-7_27"},{"issue":"1","key":"1157_CR33","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"1157_CR34","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1157_CR35","doi-asserted-by":"publisher","unstructured":"Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European Conference on computer vision, Springer, pp 818\u2013833, https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"1157_CR36","unstructured":"Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in PyTorch. In: NIPS 2017 autodiff workshop: the future of gradient-based machine learning software and techniques"},{"key":"1157_CR37","unstructured":"Paszke A, Suhan A, Meurer A, Gross S (2019) Pretrained models from torchvision. https:\/\/github.com\/pytorch\/vision\/tree\/master\/torchvision. Accessed 3 Apr 2019"},{"key":"1157_CR38","doi-asserted-by":"crossref","unstructured":"He T, Zhang Z, Zhang H, Zhang Z, Xie J, Li M (2019) Bag of tricks for image classification with convolutional neural networks. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 558\u2013567","DOI":"10.1109\/CVPR.2019.00065"},{"key":"1157_CR39","unstructured":"Caetano C, Avila S, Guimaraes S, Ara\u00fajo AdA (2014) Pornography detection using bossanova video descriptor. In: 2014 22nd European Signal Processing Conference (EUSIPCO), IEEE, pp 1681\u20131685"},{"key":"1157_CR40","doi-asserted-by":"publisher","unstructured":"Agastya IMA, Setyanto A, Handayani DOD, et\u00a0al. (2018) Convolutional neural network for pornographic images classification. In: 2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACCA), IEEE, pp 1\u20135, https:\/\/doi.org\/10.1109\/ICACCAF.2018.8776843","DOI":"10.1109\/ICACCAF.2018.8776843"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-020-01157-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-020-01157-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-020-01157-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T23:39:34Z","timestamp":1625528374000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-020-01157-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,6]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["1157"],"URL":"https:\/\/doi.org\/10.1007\/s13042-020-01157-9","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,6]]},"assertion":[{"value":"27 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}