{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:58:19Z","timestamp":1777654699212,"version":"3.51.4"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031250552","type":"print"},{"value":"9783031250569","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-25056-9_30","type":"book-chapter","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T12:09:56Z","timestamp":1676376596000},"page":"467-482","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Unrestricted Black-Box Adversarial Attack Using GAN with\u00a0Limited Queries"],"prefix":"10.1007","author":[{"given":"Dongbin","family":"Na","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sangwoo","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jong","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"key":"30_CR1","doi-asserted-by":"crossref","unstructured":"Abdal, R., Qin, Y., Wonka, P.: Image2StyleGAN: how to embed images into the StyleGAN latent space? In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4432\u20134441 (2019)","DOI":"10.1109\/ICCV.2019.00453"},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"Abdal, R., Qin, Y., Wonka, P.: Image2StyleGAN++: how to edit the embedded images? In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8296\u20138305 (2020)","DOI":"10.1109\/CVPR42600.2020.00832"},{"key":"30_CR3","doi-asserted-by":"crossref","unstructured":"Alzantot, M., Sharma, Y., Elgohary, A., Ho, B., Srivastava, M.B., Chang, K.: Generating natural language adversarial examples. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (2018)","DOI":"10.18653\/v1\/D18-1316"},{"key":"30_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2019.101663","volume":"89","author":"MK Alzaylaee","year":"2020","unstructured":"Alzaylaee, M.K., Yerima, S.Y., Sezer, S.: DL-Droid: deep learning based Android malware detection using real devices. Comput. Secur. 89, 101663 (2020)","journal-title":"Comput. Secur."},{"key":"30_CR5","unstructured":"Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples. In: International Conference on Machine Learning, pp. 274\u2013283. PMLR (2018)"},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Balaban, S.: Deep learning and face recognition: the state of the art. In: Biometric and Surveillance Technology for Human and Activity Identification XII, vol. 9457, p. 94570B. International Society for Optics and Photonics (2015)","DOI":"10.1117\/12.2181526"},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Bau, D., et al.: Seeing what a GAN cannot generate. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4502\u20134511 (2019)","DOI":"10.1109\/ICCV.2019.00460"},{"key":"30_CR8","unstructured":"Bhattad, A., Chong, M.J., Liang, K., Li, B., Forsyth, D.A.: Unrestricted adversarial examples via semantic manipulation. In: 8th International Conference on Learning Representations, ICLR 2020 (2020)"},{"key":"30_CR9","unstructured":"Brendel, W., Rauber, J., Bethge, M.: Decision-based adversarial attacks: reliable attacks against black-box machine learning models. In: 6th International Conference on Learning Representations, ICLR 2018 (2018)"},{"key":"30_CR10","unstructured":"Brendel, W., Rauber, J., K\u00fcmmerer, M., Ustyuzhaninov, I., Bethge, M.: Accurate, reliable and fast robustness evaluation. In: Annual Conference on Neural Information Processing Systems 2019 (2019)"},{"key":"30_CR11","unstructured":"Brown, T.B., Carlini, N., Zhang, C., Olsson, C., Christiano, P., Goodfellow, I.: Unrestricted adversarial examples. arXiv preprint arXiv:1809.08352 (2018)"},{"key":"30_CR12","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 39\u201357 (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"30_CR13","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Audio adversarial examples: targeted attacks on speech-to-text. In: 2018 IEEE Security and Privacy Workshops (SPW), pp. 1\u20137 (2018)","DOI":"10.1109\/SPW.2018.00009"},{"key":"30_CR14","doi-asserted-by":"crossref","unstructured":"Chen, J., Jordan, M.I., Wainwright, M.J.: HopSkipJumpAttack: a query-efficient decision-based attack. In: 2020 IEEE Symposium on Security and Privacy (SP), pp. 1277\u20131294 (2020)","DOI":"10.1109\/SP40000.2020.00045"},{"key":"30_CR15","unstructured":"Cohen, J., Rosenfeld, E., Kolter, Z.: Certified adversarial robustness via randomized smoothing. In: International Conference on Machine Learning, pp. 1310\u20131320 (2019)"},{"key":"30_CR16","unstructured":"Donahue, J., Kr\u00e4henb\u00fchl, P., Darrell, T.: Adversarial feature learning. CoRR abs\/1605.09782 (2016)"},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Ebrahimi, J., Rao, A., Lowd, D., Dou, D.: HotFlip: white-box adversarial examples for text classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics (2018)","DOI":"10.18653\/v1\/P18-2006"},{"key":"30_CR18","unstructured":"Ghiasi, A., Shafahi, A., Goldstein, T.: Breaking certified defenses: semantic adversarial examples with spoofed robustness certificates. In: 8th International Conference on Learning Representations, ICLR 2020 (2020)"},{"key":"30_CR19","unstructured":"Goodfellow, I.J., et al.: Generative adversarial nets. In: Annual Conference on Neural Information Processing Systems 2014 (2014)"},{"key":"30_CR20","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: 3rd International Conference on Learning Representations, ICLR 2015 (2015)"},{"issue":"3","key":"30_CR21","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1002\/rob.21918","volume":"37","author":"S Grigorescu","year":"2020","unstructured":"Grigorescu, S., Trasnea, B., Cocias, T., Macesanu, G.: A survey of deep learning techniques for autonomous driving. J. Field Robot. 37(3), 362\u2013386 (2020)","journal-title":"J. Field Robot."},{"key":"30_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"30_CR23","doi-asserted-by":"crossref","unstructured":"Hosseini, H., Poovendran, R.: Semantic adversarial examples. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1614\u20131619 (2018)","DOI":"10.1109\/CVPRW.2018.00212"},{"key":"30_CR24","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"30_CR25","doi-asserted-by":"crossref","unstructured":"Huang, Y., et al.: CurricularFace: adaptive curriculum learning loss for deep face recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5901\u20135910 (2020)","DOI":"10.1109\/CVPR42600.2020.00594"},{"key":"30_CR26","unstructured":"Ilyas, A., Engstrom, L., Athalye, A., Lin, J.: Black-box adversarial attacks with limited queries and information. In: International Conference on Machine Learning, pp. 2137\u20132146. PMLR (2018)"},{"key":"30_CR27","unstructured":"Kakizaki, K., Yoshida, K.: Adversarial image translation: unrestricted adversarial examples in face recognition systems. In: Proceedings of the Workshop on Artificial Intelligence Safety, Co-Located with 34th AAAI 2020 (2020)"},{"key":"30_CR28","unstructured":"Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: Proceedings of International Conference on Learning Representations (ICLR 2018) (2018)"},{"key":"30_CR29","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"30_CR30","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110\u20138119 (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"30_CR31","doi-asserted-by":"crossref","unstructured":"Lee, C.H., Liu, Z., Wu, L., Luo, P.: MaskGAN: towards diverse and interactive facial image manipulation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00559"},{"key":"30_CR32","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: 6th International Conference on Learning Representations, ICLR 2018 (2018)"},{"key":"30_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/978-3-030-65299-9_3","volume-title":"Information Security Applications","author":"D Na","year":"2020","unstructured":"Na, D., Park, N., Ji, S., Kim, J.: CAPTCHAs are still in danger: an efficient scheme to bypass adversarial CAPTCHAs. In: You, I. (ed.) WISA 2020. LNCS, vol. 12583, pp. 31\u201344. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-65299-9_3"},{"key":"30_CR34","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: 2016 IEEE European Symposium on Security and Privacy (EuroS &P), pp. 372\u2013387 (2016)","DOI":"10.1109\/EuroSP.2016.36"},{"key":"30_CR35","unstructured":"Poursaeed, O., Jiang, T., Goshu, Y., Yang, H., Belongie, S., Lim, S.N.: Fine-grained synthesis of unrestricted adversarial examples. arXiv preprint arXiv:1911.09058 (2019)"},{"key":"30_CR36","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: 4th International Conference on Learning Representations, ICLR 2016 (2016)"},{"key":"30_CR37","doi-asserted-by":"crossref","unstructured":"Richardson, E., et al.: Encoding in style: a StyleGAN encoder for image-to-image translation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2287\u20132296 (2021)","DOI":"10.1109\/CVPR46437.2021.00232"},{"issue":"7","key":"30_CR38","doi-asserted-by":"publisher","first-page":"6095","DOI":"10.1109\/TCYB.2021.3071395","volume":"52","author":"C Shi","year":"2021","unstructured":"Shi, C., et al.: Adversarial CAPTCHAs. IEEE Trans. Cybern. 52(7), 6095\u20136108 (2021)","journal-title":"IEEE Trans. Cybern."},{"key":"30_CR39","unstructured":"Song, Y., Shu, R., Kushman, N., Ermon, S.: Constructing unrestricted adversarial examples with generative models. In: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018 (2018)"},{"key":"30_CR40","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: International Conference on Learning Representations (2014)"},{"key":"30_CR41","doi-asserted-by":"crossref","unstructured":"Tan, M., et al.: MnasNet: platform-aware neural architecture search for mobile. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2820\u20132828 (2019)","DOI":"10.1109\/CVPR.2019.00293"},{"issue":"4","key":"30_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450626.3459838","volume":"40","author":"O Tov","year":"2021","unstructured":"Tov, O., Alaluf, Y., Nitzan, Y., Patashnik, O., Cohen-Or, D.: Designing an encoder for StyleGAN image manipulation. ACM Trans. Graph. (TOG) 40(4), 1\u201314 (2021)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"30_CR43","doi-asserted-by":"crossref","unstructured":"Wang, R., et al.: Amora: black-box adversarial morphing attack. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 1376\u20131385 (2020)","DOI":"10.1145\/3394171.3413544"},{"key":"30_CR44","unstructured":"Wong, E., Kolter, J.Z.: Provable defenses against adversarial examples via the convex outer adversarial polytope. In: Proceedings of the 35th International Conference on Machine Learning, ICML 2018 (2018)"},{"key":"30_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"30_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1007\/978-3-030-58520-4_35","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Zhu","year":"2020","unstructured":"Zhu, J., Shen, Y., Zhao, D., Zhou, B.: In-domain GAN inversion for real image editing. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12362, pp. 592\u2013608. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58520-4_35"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25056-9_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:34:35Z","timestamp":1710268475000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25056-9_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250552","9783031250569"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25056-9_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"0","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":"28% - 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":"3.21","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":"3.91","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":"From the workshops, 367 reviewed full papers have been selected for publication","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)"}}]}}