{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:51:36Z","timestamp":1743133896528,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811579806"},{"type":"electronic","value":"9789811579813"}],"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\/978-981-15-7981-3_16","type":"book-chapter","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T16:06:38Z","timestamp":1597939598000},"page":"247-258","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Convolutional Neural Network Visualization in Adversarial Example Attack"],"prefix":"10.1007","author":[{"given":"Chenshuo","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuli","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"16_CR1","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/978-3-642-40994-3_25","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"B Biggio","year":"2013","unstructured":"Biggio, B., et al.: Evasion attacks against machine learning at test time. In: Blockeel, H., Kersting, K., Nijssen, S., \u017delezn\u00fd, F. (eds.) ECML PKDD 2013. LNCS (LNAI), vol. 8190, pp. 387\u2013402. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40994-3_25"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Papernot, N., Mcdaniel, P., Wu, X., et al.: Distillation as a defense to adversarial perturbations against deep neural networks. In: Proceedings of the IEEE Symposium on Security and Privacy, pp. 582\u2013598 (2016)","DOI":"10.1109\/SP.2016.41"},{"key":"16_CR3","first-page":"171","volume":"2","author":"P Li","year":"2018","unstructured":"Li, P., Zhao, W., Liu, Q., et al.: Review of machine learning security and its defense technology. Comput. Sci. Explor. 2, 171\u2013184 (2018)","journal-title":"Comput. Sci. Explor."},{"key":"16_CR4","unstructured":"Marco, T.R., Sameer, S., Carlos, G.: Why should i trust you?: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)"},{"key":"16_CR5","first-page":"35","volume":"9","author":"Y Qiu","year":"2018","unstructured":"Qiu, Y., Li, S.: Security threat analysis and solutions for the development and application of artificial intelligence. Netinfo Secur. 9, 35\u201341 (2018)","journal-title":"Netinfo Secur."},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Chu, L., Hu, X., Hu, J., Wang, L.J., et al.: Exact and consistent interpretation for piecewise linear neural networks: a closed form solution. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1244\u20131253 (2018)","DOI":"10.1145\/3219819.3220063"},{"issue":"7553","key":"16_CR7","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"16_CR8","first-page":"10","volume":"9","author":"Y Yu","year":"2018","unstructured":"Yu, Y., Ding, L., Chen, Z.: Research on attacks and defenses towards machine learning systems. Netinfo Secur. 9, 10\u201318 (2018)","journal-title":"Netinfo Secur."},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Chris, O., Arvind, S.: The Building Blocks of Interpretability [OL]. https:\/\/opensource.googleblog.com\/2018\/03\/the-building-blocks-of-interpretability.html. 3 June 2018","DOI":"10.23915\/distill.00010"},{"issue":"1","key":"16_CR10","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1631\/FITEE.1700808","volume":"19","author":"Q Zhang","year":"2018","unstructured":"Zhang, Q., Zhu, S.: Visual interpretability for deep learning: a survey. Front. Inf. Technol. Electron. Eng. 19(1), 27\u201339 (2018)","journal-title":"Front. Inf. Technol. Electron. Eng."},{"issue":"10","key":"16_CR11","first-page":"1837","volume":"41","author":"Y Li","year":"2019","unstructured":"Li, Y., Yan, Z., Yan, G.: A edge-based 2-channel convolutional neural and its visualization. Comput. Eng. Sci. 41(10), 1837\u20131845 (2019)","journal-title":"Comput. Eng. Sci."},{"key":"16_CR12","unstructured":"Zhang, S., Zuo, X., Liu, J.: The Problem of the Adversarial Examples in Deep Learning [OL]. http:\/\/kns.cnki.net\/kcms\/detail\/11.1826.2018-1-20"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., et al.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"16_CR14","unstructured":"Amit, D., Karthikeyan, S., Ronny, L., Peder, O.: Improving Simple Models with Confidence Profiles[OL]. https:\/\/arxiv.org\/pdf\/1807.07506.pdf. 19 June 2018"},{"key":"16_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Taylor, G.W., Fergus, R.: Adaptive deconvolutional networks for mid and high level feature learning. In: Proceedings of 2011 IEEE International Conference on Computer Vision, pp. 2018\u20132025 (2011)","DOI":"10.1109\/ICCV.2011.6126474"},{"key":"16_CR17","unstructured":"Ramprasaath, R.S., Michael, C., Abhishek, D., Devi, P., Ramakrishna, V., Dhruv, B.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)"}],"container-title":["Communications in Computer and Information Science","Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-7981-3_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T13:32:08Z","timestamp":1710250328000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-15-7981-3_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811579806","9789811579813"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-7981-3_16","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"20 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPCSEE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference of Pioneering Computer Scientists, Engineers and Educators","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiyuan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"21 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpcsee2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2020.icpcsee.org","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":"392","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":"74","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":"24","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":"19% - 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","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":"5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}