{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T10:42:34Z","timestamp":1773744154317,"version":"3.50.1"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030553036","type":"print"},{"value":"9783030553043","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-55304-3_24","type":"book-chapter","created":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T16:04:03Z","timestamp":1596816243000},"page":"461-475","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation"],"prefix":"10.1007","author":[{"given":"Bedeuro","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sharif","family":"Abuadbba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyoungshick","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,6]]},"reference":[{"issue":"1","key":"24_CR1","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s11416-016-0287-x","volume":"14","author":"A Annadatha","year":"2016","unstructured":"Annadatha, A., Stamp, M.: Image spam analysis and detection. J. Comput. Virol. Hacking Tech. 14(1), 39\u201352 (2016). https:\/\/doi.org\/10.1007\/s11416-016-0287-x","journal-title":"J. Comput. Virol. Hacking Tech."},{"issue":"1","key":"24_CR2","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s10462-011-9280-4","volume":"40","author":"A Attar","year":"2013","unstructured":"Attar, A., Rad, R.M., Atani, R.E.: A survey of image spamming and filtering techniques. Artif. Intell. Rev. 40(1), 71\u2013105 (2013)","journal-title":"Artif. Intell. Rev."},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Bappy, J.H., Roy-Chowdhury, A.K.: CNN based region proposals for efficient object detection. In: Proceeding of the 23rd International Conference on Image Processing, pp. 3658\u20133662 (2016)","DOI":"10.1109\/ICIP.2016.7533042"},{"issue":"10","key":"24_CR4","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(10), 281\u2013305 (2012)","journal-title":"J. Mach. Learn. Res."},{"issue":"10","key":"24_CR5","doi-asserted-by":"publisher","first-page":"1436","DOI":"10.1016\/j.patrec.2011.03.022","volume":"32","author":"B Biggio","year":"2011","unstructured":"Biggio, B., Fumera, G., Pillai, I., Roli, F.: A survey and experimental evaluation of image spam filtering techniques. Pattern Recognit. Lett. 32(10), 1436\u20131446 (2011)","journal-title":"Pattern Recognit. Lett."},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"24_CR7","unstructured":"Dredze, M., Gevaryahu, R., Elias-Bachrach, A.: Learning fast classifiers for image spam. In: Proceedings of the 4th Conference on Email and Anti-Spam, pp. 487\u2013493 (2007)"},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Fatichah, C., Lazuardi, W.F., Navastara, D.A., Suciati, N., Munif, A.: Image spam detection on instagram using convolutional neural network. In: Proceedings of the 3rd Conference on Intelligent and Interactive Computing, pp. 295\u2013303 (2019)","DOI":"10.1007\/978-981-13-6031-2_19"},{"key":"24_CR9","first-page":"2699","volume":"7","author":"G Fumera","year":"2006","unstructured":"Fumera, G., Pillai, I., Roli, F.: Spam filtering based on the analysis of text information embedded into images. J. Mach. Learn. Res. 7, 2699\u20132720 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"24_CR10","unstructured":"Gao, Y., et al.: Image spam hunter. In: Proceeding of the 32nd International Conference on Acoustics, Speech and Signal Processing, pp. 1765\u20131768 (2008)"},{"issue":"2","key":"24_CR11","first-page":"1696","volume":"6","author":"A Ismail","year":"2019","unstructured":"Ismail, A., Khawandi, S., Abdallah, F.: Image spam detection: problem and existing solution. Int. Res. J. Eng. Technol. 6(2), 1696\u20131710 (2019)","journal-title":"Int. Res. J. Eng. Technol."},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, H., Lee, J.H.: Analysis and comparison of fax spam detection algorithms. In: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication, pp. 1\u20134 (2017)","DOI":"10.1145\/3022227.3022284"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Klangpraphant, P., Bhattarakosol, P.: PIMSI: a partial image SPAM inspector. In: Proceedings of the 5th International Conference on Future Information Technology, pp. 1\u20136 (2010)","DOI":"10.1109\/FUTURETECH.2010.5482767"},{"issue":"6","key":"24_CR14","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":"24_CR15","doi-asserted-by":"crossref","unstructured":"Kumar, P., Biswas, M.: SVM with Gaussian kernel-based image spam detection on textual features. In: Proceedings of the 3rd International Conference on Computational Intelligence and Communication Technology, pp. 1\u20136 (2017)","DOI":"10.1109\/CIACT.2017.7977283"},{"key":"24_CR16","unstructured":"Leszczynski, M.: Emails going to spam? 12 reasons why that happens and what you can do about it (2019). https:\/\/www.getresponse.com\/blog\/why-emails-go-to-spam"},{"key":"24_CR17","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the 30th International Conference on Machine Learning, pp. 1\u20136 (2013)"},{"key":"24_CR18","doi-asserted-by":"crossref","unstructured":"Ng, A.Y.: Feature selection, L 1 vs. L 2 regularization, and rotational invariance. In: Proceedings of the 21st International Conference on Machine learning, pp. 1\u20138 (2004)","DOI":"10.1145\/1015330.1015435"},{"key":"24_CR19","doi-asserted-by":"crossref","unstructured":"Shang, E.X., Zhang, H.G.: Image spam classification based on convolutional neural network. In: Proceedings of the 15th International Conference on Machine Learning and Cybernetics, pp. 398\u2013403 (2016)","DOI":"10.1109\/ICMLC.2016.7860934"},{"issue":"1","key":"24_CR20","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 60 (2019)","journal-title":"J. Big Data"},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"Soranamageswari, M., Meena, C.: Statistical feature extraction for classification of image spam using artificial neural networks. In: Proceedings of the 2nd International Conference on Machine Learning and Computing, pp. 101\u2013105 (2010)","DOI":"10.1109\/ICMLC.2010.72"},{"issue":"1","key":"24_CR22","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Information Security and Privacy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-55304-3_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T17:46:25Z","timestamp":1619199985000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-55304-3_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030553036","9783030553043"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-55304-3_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"6 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACISP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Conference on Information Security and Privacy","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Perth, WA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","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":"30 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acisp2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/acisp2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"151","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,7","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}