{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T13:39:51Z","timestamp":1726061991524},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030346362"},{"type":"electronic","value":"9783030346379"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-34637-9_10","type":"book-chapter","created":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T19:04:15Z","timestamp":1575572655000},"page":"136-150","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LET-Attack: Latent Encodings of Normal-Data Manifold Transferring to Adversarial Examples"],"prefix":"10.1007","author":[{"given":"Jie","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,6]]},"reference":[{"key":"10_CR1","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., et al.: Intriguing properties of neural networks. Comput. Sci. (2013)"},{"key":"10_CR2","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. Comput. Sci. (2014)"},{"key":"10_CR3","unstructured":"Tram\u00e8r, F., Papernot, N., Goodfellow, I., et al.: The space of transferable adversarial examples (2017)"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images (2014)","DOI":"10.1109\/CVPR.2015.7298640"},{"issue":"3","key":"10_CR5","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1007\/s10994-017-5663-3","volume":"107","author":"A Fawzi","year":"2015","unstructured":"Fawzi, A., Fawzi, O., Frossard, P.: Analysis of classifiers\u2019 robustness to adversarial perturbations. Mach. Learn. 107(3), 481\u2013508 (2015)","journal-title":"Mach. Learn."},{"key":"10_CR6","unstructured":"Rauber, J., Brendel, W., Bethge, M.: Foolbox: a python toolbox to benchmark the robustness of machine learning models (2017)"},{"key":"10_CR7","unstructured":"Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial examples in the physical world (2016)"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Evtimov, I., Eykholt, K., Fernandes, E., et al.: Robust physical-world attacks on deep learning models (2018)","DOI":"10.1109\/CVPR.2018.00175"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Rozsa, A., G\u00fcnther, M., Rudd, E.M., et al.: Facial attributes: accuracy and adversarial robustness. Pattern Recognit. Lett., S0167865517303926 (2018)","DOI":"10.1016\/j.patrec.2017.10.024"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Ross, A.S., Doshi-Velez, F.: Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients (2017)","DOI":"10.1609\/aaai.v32i1.11504"},{"key":"10_CR11","unstructured":"Sinha, A., Namkoong, H., Duchi, J.: Certifying some distributional robustness with principled adversarial training (2017)"},{"key":"10_CR12","unstructured":"Tram\u00e8r, F., Kurakin, A., Papernot, N., et al.: Ensemble adversarial training: attacks and defenses (2017)"},{"key":"10_CR13","unstructured":"Ma, X., Li, B., Wang, Y., et al.: Characterizing adversarial subspaces using local intrinsic dimensionality (2018)"},{"key":"10_CR14","unstructured":"Papernot, N., Mcdaniel, P.: On the effectiveness of defensive distillation (2016)"},{"key":"10_CR15","unstructured":"Metzen, J.H., Genewein, T., Fischer, V., et al.: On detecting adversarial perturbations (2017)"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Meng, D., Chen, H.: MagNet: a two-pronged defense against adversarial examples (2017)","DOI":"10.1145\/3133956.3134057"},{"key":"10_CR17","unstructured":"Zhao, Z., Dua, D., Singh, S.: Generating natural adversarial examples (2017)"},{"key":"10_CR18","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN (2017)"},{"key":"10_CR19","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., et al.: Improved Training of Wasserstein GANs (2017)"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Papernot, N., Mcdaniel, P., Jha, S., et al.: The limitations of deep learning in adversarial settings (2015)","DOI":"10.1109\/EuroSP.2016.36"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks (2016)","DOI":"10.1109\/SP.2017.49"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Narodytska, N., Kasiviswanathan, S.P.: Simple black-box adversarial perturbations for deep networks (2016)","DOI":"10.1109\/CVPRW.2017.172"},{"key":"10_CR23","unstructured":"Hayes, J., Danezis, G.: Machine learning as an adversarial service: learning black-box adversarial examples (2017)"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Papernot, N., Mcdaniel, P., Goodfellow, I., et al.: Practical black-box attacks against machine learning (2017)","DOI":"10.1145\/3052973.3053009"},{"key":"10_CR25","unstructured":"Brendel, W., Rauber, J., Bethge, M.: Decision-based adversarial attacks: reliable attacks against black-box machine learning models (2017)"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Baluja, S., Fischer, I.: Adversarial transformation networks: learning to generate adversarial examples (2017)","DOI":"10.1609\/aaai.v32i1.11672"},{"key":"10_CR27","unstructured":"Ebrahimi, J., Lowd, D., Dou, D.: On adversarial examples for character-level neural machine translation (2018)"},{"key":"10_CR28","unstructured":"Carlini, N., Wagner, D.: Defensive distillation is not robust to adversarial examples (2016)"}],"container-title":["Lecture Notes in Computer Science","Science of Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-34637-9_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T16:40:21Z","timestamp":1665160821000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-34637-9_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030346362","9783030346379"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-34637-9_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"6 December 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SciSec","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Science of Cyber Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"scisec2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.sci-cs.net\/","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":"62","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":"20","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":"8","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":"32% - 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":"3","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)"}}]}}