{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T10:41:39Z","timestamp":1743072099120,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030922375"},{"type":"electronic","value":"9783030922382"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-92238-2_16","type":"book-chapter","created":{"date-parts":[[2021,12,4]],"date-time":"2021-12-04T22:02:35Z","timestamp":1638655355000},"page":"188-200","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["EvoBA: An Evolution Strategy as a Strong Baseline for Black-Box Adversarial Attacks"],"prefix":"10.1007","author":[{"given":"Andrei","family":"Ilie","sequence":"first","affiliation":[]},{"given":"Marius","family":"Popescu","sequence":"additional","affiliation":[]},{"given":"Alin","family":"Stefanescu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,5]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Alzantot, M., Sharma, Y., Chakraborty, S., Zhang, H., Hsieh, C.J., Srivastava, M.B.: GenAttack: practical black-box attacks with gradient-free optimization. In: Proceedings of the Genetic and Evolutionary Comp. Conf. (GECCO\u201918), pp. 1111\u20131119 (2019)","DOI":"10.1145\/3321707.3321749"},{"key":"16_CR2","unstructured":"Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples. In: Proceedings of International Conference on Machine Learning (ICML\u201918), pp. 274\u2013283 (2018)"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: IEEE Symposium on Security and Privacy (SP\u201917), pp. 39\u201357. IEEE (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Chen, P.Y., Zhang, H., Sharma, Y., Yi, J., Hsieh, C.J.: Zoo: zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pp. 15\u201326 (2017)","DOI":"10.1145\/3128572.3140448"},{"key":"16_CR5","unstructured":"Dvijotham, K., Stanforth, R., Gowal, S., Mann, T.A., Kohli, P.: A dual approach to scalable verification of deep networks. In: UAI, vol. 1, p. 3 (2018)"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Eykholt, K., et al.: Robust physical-world attacks on deep learning visual classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1625\u20131634 (2018)","DOI":"10.1109\/CVPR.2018.00175"},{"key":"16_CR7","unstructured":"Goodfellow, I., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (2015)"},{"key":"16_CR8","unstructured":"Guo, C., Gardner, J.R., You, Y., Wilson, A.G., Weinberger, K.: Simple black-box adversarial attacks. In: Proceedings of International Conference on Machine Learning, pp. 2484\u20132493 (2019)"},{"key":"16_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-63387-9_1","volume-title":"Computer Aided Verification","author":"X Huang","year":"2017","unstructured":"Huang, X., Kwiatkowska, M., Wang, S., Wu, M.: Safety verification of deep neural networks. In: Majumdar, R., Kun\u010dak, V. (eds.) CAV 2017. LNCS, vol. 10426, pp. 3\u201329. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-63387-9_1"},{"key":"16_CR10","unstructured":"Ilie, A., Popescu, M., Stefanescu, A.: Robustness as inherent property of datapoints. In: AISafety Workshop, IJCAI (2020)"},{"key":"16_CR11","unstructured":"Ilyas, A., Engstrom, L., Madry, A.: Prior convictions: black-box adversarial attacks with bandits and priors (2018). arXiv:1807.07978"},{"key":"16_CR12","unstructured":"LeCun, Y., et al.: LeNet-5, convolutional neural networks (2015). http:\/\/yann.lecun.com\/exdb\/lenet"},{"key":"16_CR13","unstructured":"Meunier, L., Atif, J., Teytaud, O.: Yet another but more efficient black-box adversarial attack: tiling and evolution strategies (2019). arXiv:1910.02244"},{"key":"16_CR14","unstructured":"Papernot, N., McDaniel, P., Goodfellow, I.: Transferability in machine learning: from phenomena to black-box attacks using adversarial samples (2016). arXiv:1605.07277"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: IEEE European Symposium on Security and Privacy (EuroS&P\u201916), pp. 372\u2013387. IEEE (2016)","DOI":"10.1109\/EuroSP.2016.36"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Ruan, W., Huang, X., Kwiatkowska, M.: Reachability analysis of deep neural networks with provable guarantees (2018). arXiv:1805.02242","DOI":"10.24963\/ijcai.2018\/368"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Sharif, M., Bhagavatula, S., Bauer, L., Reiter, M.: Accessorize to a crime: real and stealthy attacks on state-of-the-art face recognition. In: Proceedings of ACM SIGSAC Conference on Computer and Communication Security (CCS\u201916), pp. 1528\u20131540 (2016)","DOI":"10.1145\/2976749.2978392"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Su, D., Zhang, H., Chen, H., Yi, J., Chen, P.Y., Gao, Y.: Is robustness the cost of accuracy? - a comprehensive study on the robustness of 18 deep image classification models. In: Proceedings of the European Conference on Computer Vision (ECCV\u201918), pp. 631\u2013648 (2018)","DOI":"10.1007\/978-3-030-01258-8_39"},{"key":"16_CR19","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks (2013). arXiv:1312.6199"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Tu, C.C., et al.: AutoZOOM: Autoencoder-based zeroth order optimization method for attacking black-box neural networks. In: Proceedings of the AAAI Conference, vol. 33, pp. 742\u2013749 (2019)","DOI":"10.1609\/aaai.v33i01.3301742"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, F., Chowdhury, S.P., Christakis, M.: Deepsearch: a simple and effective blackbox attack for deep neural networks. In: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 800\u2013812 (2020)","DOI":"10.1145\/3368089.3409750"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92238-2_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:51:09Z","timestamp":1710355869000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92238-2_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030922375","9783030922382"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92238-2_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"5 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sanur, Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2021.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"1093","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":"226","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":"177","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":"2.57","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":"6","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":"Due to the COVID-19 pandemic the conference was held online.","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)"}}]}}