{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T10:55:00Z","timestamp":1760784900898,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030596040"},{"type":"electronic","value":"9783030596057"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59605-7_11","type":"book-chapter","created":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T15:25:31Z","timestamp":1600356331000},"page":"140-155","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Mobile Application Using Deep Learning to Automatically Classify Adult-Only Images"],"prefix":"10.1007","author":[{"given":"Fanyin","family":"Zhuang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianying","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5998-222X","authenticated-orcid":false,"given":"Richard O.","family":"Sinnott","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,18]]},"reference":[{"key":"11_CR1","unstructured":"Meeker, M.: Internet Trends Report 2018. Kleiner Perkins, 30 May 2018. https:\/\/www.kleinerperkins.com\/internet-trends"},{"issue":"2","key":"11_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5565\/rev\/elcvia.78","volume":"4","author":"H Zheng","year":"2004","unstructured":"Zheng, H., Daoudi, M.: Blocking adult images based on statistical skin detection. ELCVIA Electron. Lett. Comput. Vis. Image Anal. 4(2), 1 (2004)","journal-title":"ELCVIA Electron. Lett. Comput. Vis. Image Anal."},{"key":"11_CR3","unstructured":"Blocking Images on Google: Netnanny.com (2019). https:\/\/www.netnanny.com\/kb\/255\/"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Avila, S., Thome, N., Cord, M., Valle, E., Ara\u00fajo, A.d.A.: Pooling in image representation: the visual codeword point of view. Comput. Vis. Image Underst. 117(5), 453\u2013465 (2013)","DOI":"10.1016\/j.cviu.2012.09.007"},{"issue":"5","key":"11_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3057733","volume":"8","author":"X Ou","year":"2017","unstructured":"Ou, X., Ling, H., Yu, H., Li, P., Zou, F., Liu, S.: Adult image and video recognition by a deep multicontext network and fine-to-coarse strategy. ACM Trans. Intell. Syst. Technol. 8(5), 1\u201325 (2017)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ress.2012.12.021","volume":"114","author":"S Wu","year":"2013","unstructured":"Wu, S.: A review on coarse warranty data and analysis. Reliab. Eng. Syst. Saf. 114, 1\u201311 (2013)","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Mikolajczyk, A., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: 2018 International Interdisciplinary Ph.D. Workshop (IIPhDW) (2018)","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015)","journal-title":"Neural Netw."},{"key":"11_CR9","unstructured":"Why Deep Learning over Traditional Machine Learning? Towards Data Science (2019). https:\/\/towardsdatascience.com\/why-deep-learning-is-needed-over-traditional-machine-learning-1b6a99177063"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Cayir, A., Yenidogan, I., Dag, H.: Feature extraction based on deep learning for some traditional machine learning methods. In: 2018 3rd International Conference on Computer Science and Engineering (UBMK) (2018)","DOI":"10.1109\/UBMK.2018.8566383"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhu, X., Wang, L., Guo, P.: Image classification using convolutional neural networks and kernel extreme learning machines. In: 2018 25th IEEE International Conference on Image Processing (ICIP) (2018)","DOI":"10.1109\/ICIP.2018.8451560"},{"issue":"6","key":"11_CR12","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017)","journal-title":"Commun. ACM"},{"key":"11_CR13","unstructured":"Howard, A., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications, arXiv.org (2019). https:\/\/arxiv.org\/abs\/1704.04861"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Szegedy, C, et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"11_CR15","unstructured":"A Simple Guide to the Versions of the Inception Network. Towards Data Science (2019). https:\/\/towardsdatascience.com\/a-simple-guide-to-the-versions-of-the-inception-network-7fc52b863202"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Demirovic, D., Skejic, E., Serifovic-Trbalic, A.: Performance of some image processing algorithms in tensorflow. In: 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP) (2018)","DOI":"10.1109\/IWSSIP.2018.8439714"},{"key":"11_CR17","unstructured":"TensorFlow Lite: Knowledge Transfer, 01 November 2018. http:\/\/androidkt.com\/tenserflow-lite\/"},{"issue":"10","key":"11_CR18","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"11_CR19","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/978-3-030-10925-7_24","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"M Kamp","year":"2019","unstructured":"Kamp, M., et al.: Efficient decentralized deep learning by dynamic model averaging. In: Berlingerio, M., Bonchi, F., G\u00e4rtner, T., Hurley, N., Ifrim, G. (eds.) ECML PKDD 2018. LNCS (LNAI), vol. 11051, pp. 393\u2013409. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-10925-7_24"},{"issue":"6","key":"11_CR20","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1109\/TPAMI.2007.1133","volume":"29","author":"W Hu","year":"2007","unstructured":"Hu, W., Wu, O., Chen, Z., Fu, Z., Maybank, S.: Recognition of pornographic web pages by classifying texts and images. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1019\u20131034 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR21","unstructured":"Moustafa, M.: Applying deep learning to classify pornographic images and videos,\u00a0arXiv.org (2019). https:\/\/arxiv.org\/abs\/1511.08899"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Nuraisha, S., Pratama, F.I., Budianita, A., Soeleman, M.A.: Implementation of K-NN based on histogram at image recognition for pornography detection. In: 2017 International Seminar on Application for Technology of Information and Communication (iSemantic) (2017)","DOI":"10.1109\/ISEMANTIC.2017.8251834"},{"key":"11_CR23","doi-asserted-by":"publisher","unstructured":"Lou, X., Huang, D., Fan, L., Xu, A.: An image classification algorithm based on bag of visual words and multi-kernel learning. J. Multimed. 9(2) (2014). https:\/\/doi.org\/10.4304\/jmm.9.2.269-277","DOI":"10.4304\/jmm.9.2.269-277"},{"key":"11_CR24","unstructured":"Souza, R.A.D., Almeida, R.P.D., Moldovan, A.-N., Patrocinio, Z.K.G.D., Guimaraes, S.J.F.: Gameplay genre video classification by using mid-level video representation. In: 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) (2016)"},{"key":"11_CR25","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.: Pornographic image detection utilizing deep convolutional neural networks. Neurocomputing 210, 283\u2013293 (2016)","journal-title":"Neurocomputing"},{"key":"11_CR26","unstructured":"Jung, J., Makhijani, R., Morlot, A.: Combining CNNs for detecting pornography in the absence of labeled training data. http:\/\/cs231n.stanford.edu\/reports\/2017\/pdfs\/700.pdf"},{"issue":"4","key":"11_CR27","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1007\/s10278-017-9965-6","volume":"30","author":"BJ Erickson","year":"2017","unstructured":"Erickson, B.J., Korfiatis, P., Akkus, Z., Kline, T., Philbrick, K.: Toolkits and libraries for deep learning. J. Digit. Imaging 30(4), 400\u2013405 (2017). https:\/\/doi.org\/10.1007\/s10278-017-9965-6","journal-title":"J. Digit. Imaging"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Gao, M., Lin, L., Sinnott, R.O.: A mobile application for plant recognition using deep learning. In: IEEE e-Science Conference 2017, Auckland, New Zealand, October 2017","DOI":"10.1109\/eScience.2017.15"},{"key":"11_CR29","unstructured":"Wu, F., Chen, W., Sinnott, R.O.: A mobile application for dog breed recognition and detection based on deep learning. In: IEEE\/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), Zurich, Switzerland, December 2018"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhang, X., Sinnott, R.O.: A mobile application for cat detection and breed recognition based on deep learning. In: 1st IEEE International Workshop on Artificial Intelligence for Mobile, Hangzhou, China, February 2019","DOI":"10.1109\/AI4Mobile.2019.8672684"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Wang, K., Huo, R., Jia, Y., Sinnott, R.O.: A mobile application for tree classification and canopy calculation using machine learning. In: 1st IEEE International Workshop on Artificial Intelligence for Mobile, Hangzhou, China, February 2019","DOI":"10.1109\/AI4Mobile.2019.8672699"},{"key":"11_CR32","doi-asserted-by":"crossref","unstructured":"Chen, L., Jia, Y., Sun, P., Sinnott, R.O.: Identification and classification of trucks and trailers on the road network through deep learning. In: 6th IEEE\/ACM International Conference on Big Data Computing, Applications and Technologies, Auckland, New Zealand, December 2019","DOI":"10.1145\/3365109.3368781"},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Yu, H., Song, S., Ma, S., Sinnott, R.O.: Predicting yield: identifying, classifying and counting fruit through deep learning. In: 6th IEEE\/ACM International Conference on Big Data Computing, Applications and Technologies, Auckland, New Zealand, December 2019","DOI":"10.1145\/3365109.3368766"},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Zhao, P., Lyu, X., Wei, S., Sinnott, R.O.: Crowd-counting through a cascaded, multi-task convolutional neural network. In: 6th IEEE\/ACM International Conference on Big Data Computing, Applications and Technologies, Auckland, New Zealand, December 2019","DOI":"10.1145\/3365109.3368767"},{"key":"11_CR35","unstructured":"Yang, D., Ding, X., Ye, Z., Sinnott, R.O.: Poisonous spider recognition through deep learning. In: Australia Computer Science Week, Melbourne, Australia, February 2020"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Mobile Services \u2013 AIMS 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59605-7_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T12:27:24Z","timestamp":1726662444000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59605-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030596040","9783030596057"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59605-7_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"18 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIMS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on AI and Mobile Services","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Honolulu, HI","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aimse2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ai1000.org\/2020\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}