{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T16:49:34Z","timestamp":1793206174652,"version":"4.5.0"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031171420","type":"print"},{"value":"9783031171437","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-17143-7_41","type":"book-chapter","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T04:04:22Z","timestamp":1663905862000},"page":"776-783","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["The Devil Is in\u00a0the\u00a0GAN: Backdoor Attacks and\u00a0Defenses in\u00a0Deep Generative Models"],"prefix":"10.1007","author":[{"given":"Ambrish","family":"Rawat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Killian","family":"Levacher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathieu","family":"Sinn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"key":"41_CR1","unstructured":"Lin, K., Li, D., He, X., Sun, M.-T., Zhang, Z.: Adversarial ranking for language generation. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4\u20139 Dec 2017, Long Beach, CA, USA, pp. 3155\u20133165 (2017)"},{"key":"41_CR2","unstructured":"Donahue, C., McAuley, J.J., Puckette, M.S.: Adversarial audio synthesis. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6\u20139 May 2019. OpenReview.net (2019)"},{"key":"41_CR3","doi-asserted-by":"crossref","unstructured":"Chan, C., Ginosar, S., Zhou, T., Efros, A.A.: Everybody dance now. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2 2019, pp. 5932\u20135941. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00603"},{"key":"41_CR4","unstructured":"Choi, E., Biswal, S., Malin, B.A., Duke, J., Stewart, W.F., Sun, J.: Generating multi-label discrete patient records using generative adversarial networks. In: Doshi-Velez, F., Fackler, J., Kale, D.C., Ranganath, R., Wallace, B.C., Wiens, J. (eds.) Proceedings of the Machine Learning for Health Care Conference, MLHC 2017, Boston, Massachusetts, USA, 18\u201319 Aug 2017, volume 68 of Proceedings of Machine Learning Research, pp. 286\u2013305. PMLR (2017)"},{"key":"41_CR5","unstructured":"Kingma, D.P., Mohamed, S., Rezende, D.J., Welling, M.: Semi-supervised learning with deep generative models. In: Ghahramani, Z., Welling, m., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 8\u201313 Dec 2014, Montreal, Quebec, Canada, pp. 3581\u20133589 (2014)"},{"key":"41_CR6","unstructured":"Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621 (2017)"},{"key":"41_CR7","doi-asserted-by":"crossref","unstructured":"Xu, D., Yuan, S., Zhang, L., Wu, x.: FairGAN: fairness-aware generative adversarial networks. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 570\u2013575 (2018)","DOI":"10.1109\/BigData.2018.8622525"},{"key":"41_CR8","unstructured":"Bommasani, R., et al.: On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021)"},{"key":"41_CR9","unstructured":"Giacomello, E., Loiacono, D., Mainardi, L.: Transfer brain MRI tumor segmentation models across modalities with adversarial networks. arXiv preprint arXiv:1910.02717 (2019)"},{"key":"41_CR10","unstructured":"Zhao, m., Cong, Y., Carin, L.: On leveraging pretrained GANs for generation with limited data. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13\u201318 July 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pp. 11340\u201311351. PMLR (2020)"},{"key":"41_CR11","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.patcog.2018.07.023","volume":"84","author":"B Biggio","year":"2018","unstructured":"Biggio, B., Roli, F.: Wild patterns: ten years after the rise of adversarial machine learning. Pattern Recogn. 84, 317\u2013331 (2018)","journal-title":"Pattern Recogn."},{"key":"41_CR12","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P.D., Sinha, A., Wellman, M.P: Sok: security and privacy in machine learning. In: 2018 IEEE European Symposium on Security and Privacy, EuroS &P 2018, London, United Kingdom, 24\u201326 Apr 2018, p. 399\u2013414. IEEE (2018)","DOI":"10.1109\/EuroSP.2018.00035"},{"issue":"1","key":"41_CR13","doi-asserted-by":"publisher","first-page":"133","DOI":"10.2478\/popets-2019-0008","volume":"2019","author":"J Hayes","year":"2019","unstructured":"Hayes, J., Melis, L., Danezis, G., De Cristofaro, E.: LOGAN: membership inference attacks against generative models. Proc. Priv. Enhancing Technol. 2019(1), 133\u2013152 (2019)","journal-title":"Proc. Priv. Enhancing Technol."},{"key":"41_CR14","doi-asserted-by":"crossref","unstructured":"Chen,D., Yu, N., Zhang, Y., Fritz, M.: GAN-Leaks: a taxonomy of membership inference attacks against generative models. In: Ligatti, J., Ou, J., Katz, J., Vigna, G. (eds.) CCS 2020: 2020 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, USA, 9\u201313 Nov 2020, pp. 343\u2013362. ACM (2020)","DOI":"10.1145\/3372297.3417238"},{"issue":"4","key":"41_CR15","doi-asserted-by":"publisher","first-page":"232","DOI":"10.2478\/popets-2019-0067","volume":"2019","author":"B Hilprecht","year":"2019","unstructured":"Hilprecht, B., H\u00e4rterich, M., Bernau, D.: Monte Carlo and reconstruction membership inference attacks against generative models. Proc. Priv. Enhancing Technol. 2019(4), 232\u2013249 (2019)","journal-title":"Proc. Priv. Enhancing Technol."},{"key":"41_CR16","unstructured":"Choi, k., Grover, A., Singh, T., Shu, R., Ermon, S.: Fair generative modeling via weak supervision. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13\u201318 July 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pp. 1887\u20131898. PMLR (2020)"},{"key":"41_CR17","unstructured":"Salem, A., Sautter, A., Backes, M., Humbert, M., Zhang, Y.: BAAAN: backdoor attacks against autoencoder and GAN-based machine learning models. arXiv preprint arXiv:2010.03007 (2020)"},{"key":"41_CR18","unstructured":"Goodfellow, I.J., et al.: Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014)"},{"key":"41_CR19","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, 14\u201316 Apr 2014, Conference Track Proceedings (2014)"},{"key":"41_CR20","doi-asserted-by":"crossref","unstructured":"Xu, X., Wang, Q., Li, H., Borisov, N., Gunter, C.A., Li, B.: Detecting AI trojans using meta neural analysis. In: 42nd IEEE Symposium on Security and Privacy, SP 2021, San Francisco, CA, USA, 24\u201327 May 2021, pp. 103\u2013120. IEEE (2021)","DOI":"10.1109\/SP40001.2021.00034"},{"key":"41_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/978-3-030-00470-5_13","volume-title":"Research in Attacks, Intrusions, and Defenses","author":"K Liu","year":"2018","unstructured":"Liu, K., Dolan-Gavitt, B., Garg, S.: Fine-pruning: defending against backdooring attacks on deep neural networks. In: Bailey, M., Holz, T., Stamatogiannakis, M., Ioannidis, S. (eds.) RAID 2018. LNCS, vol. 11050, pp. 273\u2013294. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00470-5_13"},{"key":"41_CR22","unstructured":"Li, Y., Lyu, X., Koren, N., Lyu, L., Li, B., Ma, X.: Neural attention distillation: erasing backdoor triggers from deep neural networks. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3\u20137 May 2021. OpenReview.net (2021)"},{"key":"41_CR23","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 4401\u20134410. Computer Vision Foundation \/ IEEE (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"issue":"11","key":"41_CR24","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"41_CR25","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, 2\u20134 May 2016, Conference Track Proceedings (2016)"},{"key":"41_CR26","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Radford, A., Chen, X., Cheung, V.: Improved techniques for training GANs (2016)"},{"key":"41_CR27","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: Guyon, I. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4\u20139 Dec 2017, Long Beach, CA, USA, pp. 6626\u20136637 (2017)"},{"key":"41_CR28","unstructured":"Aguinaldo, A., Chiang, P-Y., Gain, A., Patil, A., Pearson, K., Feizi, S.: Compressing GANs using knowledge distillation. arXiv preprint arXiv:1902.00159 (2019)"},{"key":"41_CR29","doi-asserted-by":"crossref","unstructured":"Abdal, R., Qin, Y., Wonka, P.: Image2stylegan: how to embed images into the styleGAN latent space? In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2 2019, pp. 4431\u20134440. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00453"},{"key":"41_CR30","unstructured":"Yu, C., Pool, J.: Self-supervised GAN compression. arXiv preprint arXiv:2007.01491 (2020)"}],"container-title":["Lecture Notes in Computer Science","Computer Security \u2013 ESORICS 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17143-7_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T04:13:43Z","timestamp":1663906423000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17143-7_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031171420","9783031171437"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17143-7_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ESORICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Symposium on Research in Computer Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Copenhagen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Denmark","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":"26 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"esorics2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/esorics2022.compute.dtu.dk\/","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":"562","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":"104","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":"6","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":"3.4","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":"12","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)"}}]}}