{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T16:25:03Z","timestamp":1778171103528,"version":"3.51.4"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031736490","type":"print"},{"value":"9783031736506","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73650-6_23","type":"book-chapter","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T18:16:24Z","timestamp":1732126584000},"page":"396-412","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Cocktail Universal Adversarial Attack on\u00a0Deep Neural Networks"],"prefix":"10.1007","author":[{"given":"Shaoxin","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofeng","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Che","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xintong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingyang","family":"Chu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"23_CR1","unstructured":"Akhtar, N., Jalwana, M.A., Bennamoun, M., Mian, A.: Label universal targeted attack. arXiv preprint arXiv:1905.11544 (2019)"},{"key":"23_CR2","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 (2018)"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Benz, P., Zhang, C., Imtiaz, T., Kweon, I.S.: Double targeted universal adversarial perturbations. In: Proceedings of the Asian Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-69538-5_18"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy, pp. 39\u201357 (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"23_CR5","unstructured":"Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: a survey. arXiv preprint arXiv:2005.08087 (2020)"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Deng, Y., Karam, L.J.: Universal adversarial attack via enhanced projected gradient descent. In: IEEE International Conference on Image Processing, pp. 1241\u20131245 (2020)","DOI":"10.1109\/ICIP40778.2020.9191288"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., Li, J.: Boosting adversarial attacks with momentum. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9185\u20139193 (2018)","DOI":"10.1109\/CVPR.2018.00957"},{"key":"23_CR9","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"5","key":"23_CR10","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1090\/S0002-9904-1964-11178-2","volume":"70","author":"AA Goldstein","year":"1964","unstructured":"Goldstein, A.A.: Convex programming in hilbert space. Bull. Am. Math. Soc. 70(5), 709\u2013710 (1964)","journal-title":"Bull. Am. Math. Soc."},{"key":"23_CR11","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Graham, B., El-Nouby, A., Touvron, H., Stock, P., Joulin, A., J\u00e9gou, H., Douze, M.: Levit: a vision transformer in convnet\u2019s clothing for faster inference. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12259\u201312269 (2021)","DOI":"10.1109\/ICCV48922.2021.01204"},{"key":"23_CR13","unstructured":"Gupta, T., Sinha, A., Kumari, N., Singh, M., Krishnamurthy, B.: A method for computing class-wise universal adversarial perturbations. arXiv preprint arXiv:1912.00466 (2019)"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Hayes, J., Danezis, G.: Learning universal adversarial perturbations with generative models. In: IEEE Security and Privacy Workshops, pp. 43\u201349 (2018)","DOI":"10.1109\/SPW.2018.00015"},{"key":"23_CR15","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":"23_CR16","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:1602.07360 (2016)"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Khrulkov, V., Oseledets, I.: Art of singular vectors and universal adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8562\u20138570 (2018)","DOI":"10.1109\/CVPR.2018.00893"},{"key":"23_CR18","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Li, M., Yang, Y., Wei, K., Yang, X., Huang, H.: Learning universal adversarial perturbation by adversarial example. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 1350\u20131358 (2022)","DOI":"10.1609\/aaai.v36i2.20023"},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Li, Y., Bai, S., Xie, C., Liao, Z., Shen, X., Yuille, A.: Regional homogeneity: towards learning transferable universal adversarial perturbations against defenses. In: Proceedings of the European Conference on Computer Vision, pp. 795\u2013813 (2020)","DOI":"10.1007\/978-3-030-58621-8_46"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Liu, H., Ji, R., Li, J., Zhang, B., Gao, Y., Wu, Y., Huang, F.: Universal adversarial perturbation via prior driven uncertainty approximation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2941\u20132949 (2019)","DOI":"10.1109\/ICCV.2019.00303"},{"key":"23_CR22","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1765\u20131773 (2017)","DOI":"10.1109\/CVPR.2017.17"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2574\u20132582 (2016)","DOI":"10.1109\/CVPR.2016.282"},{"issue":"10","key":"23_CR25","doi-asserted-by":"publisher","first-page":"2452","DOI":"10.1109\/TPAMI.2018.2861800","volume":"41","author":"KR Mopuri","year":"2018","unstructured":"Mopuri, K.R., Ganeshan, A., Babu, R.V.: Generalizable data-free objective for crafting universal adversarial perturbations. IEEE Trans. Pattern Anal. Mach. Intell. 41(10), 2452\u20132465 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR26","unstructured":"Mopuri, K.R., Garg, U., Babu, R.V.: Fast feature fool: a data independent approach to universal adversarial perturbations. arXiv preprint arXiv:1707.05572 (2017)"},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"Mopuri, K.R., Ojha, U., Garg, U., Babu, R.V.: Nag: Network for adversary generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 742\u2013751 (2018)","DOI":"10.1109\/CVPR.2018.00084"},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"Mopuri, K.R., Uppala, P.K., Babu, R.V.: Ask, acquire, and attack: data-free UAP generation using class impressions. In: Proceedings of the European Conference on Computer Vision, pp. 19\u201334 (2018)","DOI":"10.1007\/978-3-030-01240-3_2"},{"key":"23_CR29","unstructured":"Nakka1, K.K., Salzmann, M.: Learning transferable adversarial perturbations. Adv. Neural Inf. Proc. Syst. 34, 13950\u201313962 (2021)"},{"key":"23_CR30","unstructured":"Naseer, M.M., Khan, S.H., Khan, M.H., Shahbaz\u00a0Khan, F., Porikli, F.: Cross-domain transferability of adversarial perturbations. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"23_CR31","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, pp. 372\u2013387. IEEE (2016)","DOI":"10.1109\/EuroSP.2016.36"},{"key":"23_CR32","unstructured":"Park, S.M., Wei, K.A., Xiao, K., Li, J., Madry, A.: On distinctive properties of universal perturbations. arXiv preprint arXiv:2112.15329 (2021)"},{"key":"23_CR33","doi-asserted-by":"crossref","unstructured":"Poursaeed, O., Katsman, I., Gao, B., Belongie, S.: Generative adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4422\u20134431 (2018)","DOI":"10.1109\/CVPR.2018.00465"},{"key":"23_CR34","doi-asserted-by":"crossref","unstructured":"Shafahi, A., Najibi, M., Xu, Z., Dickerson, J., Davis, L.S., Goldstein, T.: Universal adversarial training. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 5636\u20135643 (2020)","DOI":"10.1609\/aaai.v34i04.6017"},{"key":"23_CR35","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"23_CR36","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"23_CR37","unstructured":"Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., Madry, A.: Robustness may be at odds with accuracy. arXiv preprint arXiv:1805.12152 (2018)"},{"key":"23_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Cd-UAP: class discriminative universal adversarial perturbation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 6754\u20136761 (2020)","DOI":"10.1609\/aaai.v34i04.6154"},{"key":"23_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14521\u201314530 (2020)","DOI":"10.1109\/CVPR42600.2020.01453"},{"key":"23_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, C., Benz, P., Karjauv, A., Kweon, I.S.: Data-free universal adversarial perturbation and black-box attack. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7868\u20137877 (2021)","DOI":"10.1109\/ICCV48922.2021.00777"},{"key":"23_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, C., Benz, P., Karjauv, A., Kweon, I.S.: Universal adversarial perturbations through the lens of deep steganography: towards a fourier perspective. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 3296\u20133304 (2021)","DOI":"10.1609\/aaai.v35i4.16441"},{"key":"23_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, C., Benz, P., Lin, C., Karjauv, A., Wu, J., Kweon, I.S.: A survey on universal adversarial attack. arXiv preprint arXiv:2103.01498 (2021)","DOI":"10.24963\/ijcai.2021\/635"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73650-6_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T19:08:24Z","timestamp":1732129704000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73650-6_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,21]]},"ISBN":["9783031736490","9783031736506"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73650-6_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,21]]},"assertion":[{"value":"21 November 2024","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":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}