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Surv."],"published-print":{"date-parts":[[2025,10,31]]},"abstract":"<jats:p>The Metaverse is a hybrid environment that integrates both physical and virtual realms. The Metaverse has been accessible due to many facilitating technologies. One of the essential technologies that contribute to the Metaverse is AIGC. It is crucial in creating artificial assets and presenting natural interactions efficiently and effectively. Nevertheless, AIGC models encounter external and internal obstacles in security, privacy, and ethics during every level of their development. To conduct a thorough analysis and investigation of risks and threats, we propose a new taxonomy system that categorizes the issues based on three primary factors: the stage of threat exposure, the specific area of the concerns, and the origin of the threats. Furthermore, we present specific unresolved questions that prompt additional investigation into the risks posed by AIGC and the steps taken to counteract them in Metaverse art creation and interactive methodologies. This thorough evaluation offers a broad perspective on the security measures AIGC uses in the Metaverse.<\/jats:p>","DOI":"10.1145\/3729419","type":"journal-article","created":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T11:28:26Z","timestamp":1744975706000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Security and Privacy Challenges of AIGC in Metaverse: A Comprehensive Survey"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1626-4054","authenticated-orcid":false,"given":"Shoulong","family":"Zhang","sequence":"first","affiliation":[{"name":"Zhongguancun Laboratory, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3009-8860","authenticated-orcid":false,"given":"Haomin","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4262-2705","authenticated-orcid":false,"given":"Kaiwen","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3876-0294","authenticated-orcid":false,"given":"Hejia","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3008-931X","authenticated-orcid":false,"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhongguancun Laboratory, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4182-1588","authenticated-orcid":false,"given":"Shuai","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China and Zhongguancun Laboratory, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,5,7]]},"reference":[{"key":"e_1_3_3_2_2","article-title":"The Falcon series of open language models","author":"Almazrouei Ebtesam","year":"2023","unstructured":"Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, M\u00e9rouane Debbah, \u00c9tienne Goffinet, Daniel Hesslow, Julien Launay, Quentin Malartic, et\u00a0al. 2023. 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In USENIX Security Symposium. 5253\u20135270."},{"key":"e_1_3_3_16_2","first-page":"267","volume-title":"USENIX Security Symposium","author":"Carlini Nicholas","year":"2019","unstructured":"Nicholas Carlini, Chang Liu, \u00dalfar Erlingsson, Jernej Kos, and Dawn Song. 2019. The secret sharer: Evaluating and testing unintended memorization in neural networks. In USENIX Security Symposium. 267\u2013284."},{"key":"e_1_3_3_17_2","article-title":"Are aligned neural networks adversarially aligned?","author":"Carlini Nicholas","year":"2023","unstructured":"Nicholas Carlini, Milad Nasr, Christopher A Choquette-Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee, Florian Tramer, et\u00a0al. 2023. 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A pathway towards responsible AI generated content. arXiv preprint arXiv:2303.01325 (2023).","journal-title":"arXiv preprint arXiv:2303.01325"},{"key":"e_1_3_3_20_2","article-title":"Challenges and remedies to privacy and security in AIGC: Exploring the potential of privacy computing, blockchain, and beyond","author":"Chen Chuan","year":"2023","unstructured":"Chuan Chen, Zhenpeng Wu, Yanyi Lai, Wenlin Ou, Tianchi Liao, and Zibin Zheng. 2023. Challenges and remedies to privacy and security in AIGC: Exploring the potential of privacy computing, blockchain, and beyond. arXiv preprint arXiv:2306.00419 (2023).","journal-title":"arXiv preprint arXiv:2306.00419"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417238"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467445"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00393"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01726"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData55660.2022.10021112"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544746"},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3242152"},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00391"},{"issue":"240","key":"e_1_3_3_29_2","first-page":"1","article-title":"PaLM: Scaling language modeling with pathways","volume":"24","author":"Chowdhery Aakanksha","year":"2023","unstructured":"Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et\u00a0al. 2023. 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Adversarial video generation on complex datasets. arXiv preprint arXiv:1907.06571 (2019).","journal-title":"arXiv preprint arXiv:1907.06571"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi9070439"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10095167"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00101"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3261988"},{"key":"e_1_3_3_36_2","first-page":"1","volume-title":"SIGGRAPH Asia","author":"Dan\u011b\u010dek Radek","year":"2023","unstructured":"Radek Dan\u011b\u010dek, Kiran Chhatre, Shashank Tripathi, Yandong Wen, Michael Black, and Timo Bolkart. 2023. Emotional speech-driven animation with content-emotion disentanglement. 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Spear or shield: Leveraging generative AI to tackle security threats of intelligent network services. arXiv preprint arXiv:2306.02384 (2023).","journal-title":"arXiv preprint arXiv:2306.02384"},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/WIFS55849.2022.9975409"},{"key":"e_1_3_3_42_2","article-title":"LayoutGPT: Compositional visual planning and generation with large language models","volume":"36","author":"Feng Weixi","year":"2024","unstructured":"Weixi Feng, Wanrong Zhu, Tsu-jui Fu, Varun Jampani, Arjun Akula, Xuehai He, Sugato Basu, Xin Eric Wang, and William Yang Wang. 2024. LayoutGPT: Compositional visual planning and generation with large language models. Advances in Neural Information Processing Systems 36 (2024).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02053"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41386-021-01132-0"},{"key":"e_1_3_3_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-33630-5_19"},{"key":"e_1_3_3_46_2","article-title":"DataComp: In search of the next generation of multimodal datasets","volume":"36","author":"Gadre Samir Yitzhak","year":"2024","unstructured":"Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, et\u00a0al. 2024. DataComp: In search of the next generation of multimodal datasets. Advances in Neural Information Processing Systems 36 (2024).","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"8","key":"e_1_3_3_47_2","first-page":"4291","article-title":"Part-level car parsing and reconstruction in single street view images","volume":"44","author":"Geng Qichuan","year":"2021","unstructured":"Qichuan Geng, Hong Zhang, Feixiang Lu, Xinyu Huang, Sen Wang, Zhong Zhou, and Ruigang Yang. 2021. Part-level car parsing and reconstruction in single street view images. 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Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33 (2020), 6840\u20136851.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_52_2","article-title":"Towards few-call model stealing via active self-paced knowledge distillation and diffusion-based image generation","author":"Hondru Vlad","year":"2023","unstructured":"Vlad Hondru and Radu Tudor Ionescu. 2023. Towards few-call model stealing via active self-paced knowledge distillation and diffusion-based image generation. arXiv preprint arXiv:2310.00096 (2023).","journal-title":"arXiv preprint arXiv:2310.00096"},{"key":"e_1_3_3_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3326337"},{"key":"e_1_3_3_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01181"},{"key":"e_1_3_3_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485832.3485838"},{"key":"e_1_3_3_56_2","doi-asserted-by":"publisher","DOI":"10.1177\/1687814019836838"},{"key":"e_1_3_3_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.02.008"},{"key":"e_1_3_3_58_2","article-title":"Mistral 7B","author":"Jiang Albert Q.","year":"2023","unstructured":"Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et\u00a0al. 2023. Mistral 7B. arXiv preprint arXiv:2310.06825 (2023).","journal-title":"arXiv preprint arXiv:2310.06825"},{"key":"e_1_3_3_59_2","first-page":"1","article-title":"Mirror world: Creating digital twins of the space and persons from video streamings","author":"Jiang Ling","year":"2023","unstructured":"Ling Jiang, Liangliang Cai, Wei Wu, and Zhong Zhou. 2023. Mirror world: Creating digital twins of the space and persons from video streamings. 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FairFace: Face attribute dataset for balanced race, gender, and age. arXiv preprint arXiv:1908.04913 (2019).","journal-title":"arXiv preprint arXiv:1908.04913"},{"key":"e_1_3_3_65_2","first-page":"12104","article-title":"Training generative adversarial networks with limited data","volume":"33","author":"Karras Tero","year":"2020","unstructured":"Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila. 2020. Training generative adversarial networks with limited data. Advances in Neural Information Processing Systems 33 (2020), 12104\u201312114.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"e_1_3_3_67_2","doi-asserted-by":"publisher","DOI":"10.1145\/3592433"},{"key":"e_1_3_3_68_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01462"},{"key":"e_1_3_3_69_2","doi-asserted-by":"publisher","DOI":"10.1145\/3557889"},{"key":"e_1_3_3_70_2","article-title":"Improved variational inference with inverse autoregressive flow","volume":"29","author":"Kingma Durk P.","year":"2016","unstructured":"Durk P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling. 2016. Improved variational inference with inverse autoregressive flow. Advances in Neural Information Processing Systems 29 (2016).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992934"},{"key":"e_1_3_3_72_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00192"},{"key":"e_1_3_3_73_2","article-title":"I see dead people: Gray-box adversarial attack on image-to-text models","author":"Lapid Raz","year":"2023","unstructured":"Raz Lapid and Moshe Sipper. 2023. I see dead people: Gray-box adversarial attack on image-to-text models. arXiv preprint arXiv:2306.07591 (2023).","journal-title":"arXiv preprint arXiv:2306.07591"},{"key":"e_1_3_3_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02045"},{"key":"e_1_3_3_75_2","unstructured":"Teven Le Scao Angela Fan Christopher Akiki Ellie Pavlick Suzana Ili\u0107 Daniel Hesslow Roman Castagn\u00e9 Alexandra Sasha Luccioni Fran\u00e7ois Yvon Matthias Gall\u00e9 et\u00a0al. 2023. BLOOM: A 176B-parameter open-access multilingual language model. (2023)."},{"key":"e_1_3_3_76_2","article-title":"All one needs to know about metaverse: A complete survey on technological singularity, virtual ecosystem, and research agenda","author":"Lee Lik-Hang","year":"2021","unstructured":"Lik-Hang Lee, Tristan Braud, Pengyuan Zhou, Lin Wang, Dianlei Xu, Zijun Lin, Abhishek Kumar, Carlos Bermejo, and Pan Hui. 2021. All one needs to know about metaverse: A complete survey on technological singularity, virtual ecosystem, and research agenda. arXiv preprint arXiv:2110.05352 (2021).","journal-title":"arXiv preprint arXiv:2110.05352"},{"key":"e_1_3_3_77_2","article-title":"HyperSDFusion: Bridging hierarchical structures in language and geometry for enhanced 3D Text2Shape generation","author":"Leng Zhiying","year":"2024","unstructured":"Zhiying Leng, Tolga Birdal, Xiaohui Liang, and Federico Tombari. 2024. HyperSDFusion: Bridging hierarchical structures in language and geometry for enhanced 3D Text2Shape generation. arXiv preprint arXiv:2403.00372 (2024).","journal-title":"arXiv preprint arXiv:2403.00372"},{"key":"e_1_3_3_78_2","article-title":"Multi-step jailbreaking privacy attacks on ChatGPT","author":"Li Haoran","year":"2023","unstructured":"Haoran Li, Dadi Guo, Wei Fan, Mingshi Xu, and Yangqiu Song. 2023. Multi-step jailbreaking privacy attacks on ChatGPT. arXiv preprint arXiv:2304.05197 (2023).","journal-title":"arXiv preprint arXiv:2304.05197"},{"key":"e_1_3_3_79_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2022.3203099"},{"key":"e_1_3_3_80_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016706"},{"key":"e_1_3_3_81_2","article-title":"A survey on federated learning systems: Vision, hype and reality for data privacy and protection","author":"Li Qinbin","year":"2021","unstructured":"Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, and Bingsheng He. 2021. A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering (2021).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_3_82_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00871"},{"key":"e_1_3_3_83_2","article-title":"VideoGen: A reference-guided latent diffusion approach for high definition text-to-video generation","author":"Li Xin","year":"2023","unstructured":"Xin Li, Wenqing Chu, Ye Wu, Weihang Yuan, Fanglong Liu, Qi Zhang, Fu Li, Haocheng Feng, Errui Ding, and Jingdong Wang. 2023. VideoGen: A reference-guided latent diffusion approach for high definition text-to-video generation. arXiv preprint arXiv:2309.00398 (2023).","journal-title":"arXiv preprint arXiv:2309.00398"},{"key":"e_1_3_3_84_2","article-title":"AIGC in China: Current developments and future outlook","author":"Li Xiangyu","year":"2023","unstructured":"Xiangyu Li, Yuqing Fan, and Shenghui Cheng. 2023. AIGC in China: Current developments and future outlook. arXiv preprint arXiv:2308.08451 (2023).","journal-title":"arXiv preprint arXiv:2308.08451"},{"key":"e_1_3_3_85_2","article-title":"LoMar: A local defense against poisoning attack on federated learning","author":"Li Xingyu","year":"2021","unstructured":"Xingyu Li, Zhe Qu, Shangqing Zhao, Bo Tang, Zhuo Lu, and Yao Liu. 2021. LoMar: A local defense against poisoning attack on federated learning. IEEE Transactions on Dependable and Secure Computing (2021).","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"e_1_3_3_86_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3182979"},{"key":"e_1_3_3_87_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"e_1_3_3_88_2","article-title":"StyleTTS 2: Towards human-level text-to-speech through style diffusion and adversarial training with large speech language models","volume":"36","author":"Li Yinghao Aaron","year":"2024","unstructured":"Yinghao Aaron Li, Cong Han, Vinay Raghavan, Gavin Mischler, and Nima Mesgarani. 2024. StyleTTS 2: Towards human-level text-to-speech through style diffusion and adversarial training with large speech language models. Advances in Neural Information Processing Systems 36 (2024).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_89_2","first-page":"1","article-title":"InterGen: Diffusion-based multi-human motion generation under complex interactions","author":"Liang Han","year":"2024","unstructured":"Han Liang, Wenqian Zhang, Wenxuan Li, Jingyi Yu, and Lan Xu. 2024. InterGen: Diffusion-based multi-human motion generation under complex interactions. International Journal of Computer Vision (2024), 1\u201321.","journal-title":"International Journal of Computer Vision"},{"key":"e_1_3_3_90_2","article-title":"EVA-GAN: Enhanced various audio generation via scalable generative adversarial networks","author":"Liao Shijia","year":"2024","unstructured":"Shijia Liao, Shiyi Lan, and Arun George Zachariah. 2024. EVA-GAN: Enhanced various audio generation via scalable generative adversarial networks. arXiv preprint arXiv:2402.00892 (2024).","journal-title":"arXiv preprint arXiv:2402.00892"},{"key":"e_1_3_3_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00037"},{"key":"e_1_3_3_92_2","doi-asserted-by":"publisher","DOI":"10.1108\/IJCS-03-2020-0007"},{"key":"e_1_3_3_93_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01972"},{"key":"e_1_3_3_94_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00853"},{"key":"e_1_3_3_95_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-17140-6_30"},{"key":"e_1_3_3_96_2","article-title":"Interactive humanoid: Online full-body motion reaction synthesis with social affordance canonicalization and forecasting","author":"Liu Yunze","year":"2023","unstructured":"Yunze Liu, Changxi Chen, and Li Yi. 2023. Interactive humanoid: Online full-body motion reaction synthesis with social affordance canonicalization and forecasting. arXiv preprint arXiv:2312.08983 (2023).","journal-title":"arXiv preprint arXiv:2312.08983"},{"key":"e_1_3_3_97_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_3_3_98_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.obhdp.2018.12.005"},{"key":"e_1_3_3_99_2","first-page":"1","volume-title":"CHI Conference on Human Factors in Computing Systems","author":"Lu Zhuoran","year":"2021","unstructured":"Zhuoran Lu and Ming Yin. 2021. Human reliance on machine learning models when performance feedback is limited: Heuristics and risks. In CHI Conference on Human Factors in Computing Systems. 1\u201316."},{"key":"e_1_3_3_100_2","article-title":"UniVL: A unified video and language pre-training model for multimodal understanding and generation","author":"Luo Huaishao","year":"2020","unstructured":"Huaishao Luo, Lei Ji, Botian Shi, Haoyang Huang, Nan Duan, Tianrui Li, Jason Li, Taroon Bharti, and Ming Zhou. 2020. UniVL: A unified video and language pre-training model for multimodal understanding and generation. arXiv preprint arXiv:2002.06353 (2020).","journal-title":"arXiv preprint arXiv:2002.06353"},{"key":"e_1_3_3_101_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2021.3110459"},{"key":"e_1_3_3_102_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/803"},{"key":"e_1_3_3_103_2","first-page":"1","article-title":"Apple Vision Pro for ophthalmology and medicine","author":"Masalkhi Mouayad","year":"2023","unstructured":"Mouayad Masalkhi, Ethan Waisberg, Joshua Ong, Nasif Zaman, Prithul Sarker, Andrew G. 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OpenAI Blog (2022)."},{"key":"e_1_3_3_110_2","article-title":"A survey on the metaverse: The state-of-the-art, technologies, applications, and challenges","author":"Ning Huansheng","year":"2023","unstructured":"Huansheng Ning, Hang Wang, Yujia Lin, Wenxi Wang, Sahraoui Dhelim, Fadi Farha, Jianguo Ding, and Mahmoud Daneshmand. 2023. A survey on the metaverse: The state-of-the-art, technologies, applications, and challenges. IEEE Internet of Things Journal (2023).","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_3_111_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2024.3372093"},{"issue":"57","key":"e_1_3_3_112_2","first-page":"1","article-title":"Normalizing flows for probabilistic modeling and inference","volume":"22","author":"Papamakarios George","year":"2021","unstructured":"George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. 2021. Normalizing flows for probabilistic modeling and inference. 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Microsoft Research (2022).","journal-title":"Microsoft Research"},{"key":"e_1_3_3_116_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.1989.118724"},{"key":"e_1_3_3_117_2","article-title":"From prompt injections to SQL injection attacks: How protected is your LLM-Integrated web application?","author":"Pedro Rodrigo","year":"2023","unstructured":"Rodrigo Pedro, Daniel Castro, Paulo Carreira, and Nuno Santos. 2023. From prompt injections to SQL injection attacks: How protected is your LLM-Integrated web application? arXiv preprint arXiv:2308.01990 (2023).","journal-title":"arXiv preprint arXiv:2308.01990"},{"key":"e_1_3_3_118_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01891"},{"key":"e_1_3_3_119_2","article-title":"Ignore previous prompt: Attack techniques for language models","author":"Perez F\u00e1bio","year":"2022","unstructured":"F\u00e1bio Perez and Ian Ribeiro. 2022. Ignore previous prompt: Attack techniques for language models. arXiv preprint arXiv:2211.09527 (2022).","journal-title":"arXiv preprint arXiv:2211.09527"},{"key":"e_1_3_3_120_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-66415-2_4"},{"key":"e_1_3_3_121_2","article-title":"Deep Voice 3: Scaling text-to-speech with convolutional sequence learning","author":"Ping Wei","year":"2017","unstructured":"Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan O. Arik, Ajay Kannan, Sharan Narang, Jonathan Raiman, and John Miller. 2017. Deep Voice 3: Scaling text-to-speech with convolutional sequence learning. arXiv preprint arXiv:1710.07654 (2017).","journal-title":"arXiv preprint arXiv:1710.07654"},{"key":"e_1_3_3_122_2","article-title":"SDXL: Improving latent diffusion models for high-resolution image synthesis","author":"Podell Dustin","year":"2023","unstructured":"Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M\u00fcller, Joe Penna, and Robin Rombach. 2023. SDXL: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952 (2023).","journal-title":"arXiv preprint arXiv:2307.01952"},{"key":"e_1_3_3_123_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683143"},{"key":"e_1_3_3_124_2","volume-title":"Workshop on New Frontiers in Adversarial Machine Learning","author":"Qi Xiangyu","year":"2023","unstructured":"Xiangyu Qi, Kaixuan Huang, Ashwinee Panda, Mengdi Wang, and Prateek Mittal. 2023. Visual adversarial examples jailbreak aligned large language models. In Workshop on New Frontiers in Adversarial Machine Learning, Vol. 1."},{"issue":"4","key":"e_1_3_3_125_2","first-page":"817","article-title":"Security in virtual-real mixing cyberspaces: A survey","volume":"54","author":"Zhou Xiaohui Liang, Shuai Li, Miao Wang, Yan Wang, Qinping Zhao, and Zhong","year":"2024","unstructured":"Xiaohui Liang, Shuai Li, Miao Wang, Yan Wang, Qinping Zhao, and Zhong Zhou. 2024. Security in virtual-real mixing cyberspaces: A survey. SCIENTIA SINICA Informationis 54, 4 (2024), 817\u2013852.","journal-title":"SCIENTIA SINICA Informationis"},{"key":"e_1_3_3_126_2","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. OpenAI archive. (2018)."},{"issue":"8","key":"e_1_3_3_127_2","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford Alec","year":"2019","unstructured":"Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog 1, 8 (2019), 9.","journal-title":"OpenAI Blog"},{"issue":"2","key":"e_1_3_3_128_2","first-page":"3","article-title":"Hierarchical text-conditional image generation with clip latents","volume":"1","author":"Ramesh Aditya","year":"2022","unstructured":"Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 1, 2 (2022), 3.","journal-title":"arXiv preprint arXiv:2204.06125"},{"key":"e_1_3_3_129_2","first-page":"8821","volume-title":"International Conference on Machine Learning","author":"Ramesh Aditya","year":"2021","unstructured":"Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-shot text-to-image generation. In International Conference on Machine Learning. PMLR, 8821\u20138831."},{"key":"e_1_3_3_130_2","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445170"},{"key":"e_1_3_3_131_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-17143-7_41"},{"key":"e_1_3_3_132_2","doi-asserted-by":"publisher","DOI":"10.3389\/frvir.2021.721933"},{"key":"e_1_3_3_133_2","article-title":"FastSpeech 2: Fast and high-quality end-to-end text to speech","author":"Ren Yi","year":"2020","unstructured":"Yi Ren, Chenxu Hu, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu. 2020. FastSpeech 2: Fast and high-quality end-to-end text to speech. arXiv preprint arXiv:2006.04558 (2020).","journal-title":"arXiv preprint arXiv:2006.04558"},{"key":"e_1_3_3_134_2","doi-asserted-by":"publisher","DOI":"10.1109\/VR51125.2022.00025"},{"key":"e_1_3_3_135_2","first-page":"1530","volume-title":"International Conference on Machine Learning","author":"Rezende Danilo","year":"2015","unstructured":"Danilo Rezende and Shakir Mohamed. 2015. Variational inference with normalizing flows. In International Conference on Machine Learning. PMLR, 1530\u20131538."},{"key":"e_1_3_3_136_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"e_1_3_3_137_2","doi-asserted-by":"publisher","DOI":"10.3390\/fi15060192"},{"key":"e_1_3_3_138_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i12.26693"},{"key":"e_1_3_3_139_2","first-page":"36479","article-title":"Photorealistic text-to-image diffusion models with deep language understanding","volume":"35","author":"Saharia Chitwan","year":"2022","unstructured":"Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L. Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et\u00a0al. 2022. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems 35 (2022), 36479\u201336494.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_140_2","article-title":"BAAAN: Backdoor attacks against autoencoder and GAN-based machine learning models","author":"Salem Ahmed","year":"2020","unstructured":"Ahmed Salem, Yannick Sautter, Michael Backes, Mathias Humbert, and Yang Zhang. 2020. BAAAN: Backdoor attacks against autoencoder and GAN-based machine learning models. arXiv preprint arXiv:2010.03007 (2020).","journal-title":"arXiv preprint arXiv:2010.03007"},{"key":"e_1_3_3_141_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01485"},{"key":"e_1_3_3_142_2","article-title":"LAION-400M: Open dataset of clip-filtered 400 million image-text pairs","author":"Schuhmann Christoph","year":"2021","unstructured":"Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki. 2021. LAION-400M: Open dataset of clip-filtered 400 million image-text pairs. arXiv preprint arXiv:2111.02114 (2021).","journal-title":"arXiv preprint arXiv:2111.02114"},{"key":"e_1_3_3_143_2","doi-asserted-by":"publisher","DOI":"10.1109\/78.650093"},{"key":"e_1_3_3_144_2","article-title":"Prompt-specific poisoning attacks on text-to-image generative models","author":"Shan Shawn","year":"2023","unstructured":"Shawn Shan, Wenxin Ding, Josephine Passananti, Haitao Zheng, and Ben Y. Zhao. 2023. Prompt-specific poisoning attacks on text-to-image generative models. arXiv preprint arXiv:2310.13828 (2023).","journal-title":"arXiv preprint arXiv:2310.13828"},{"key":"e_1_3_3_145_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8461368"},{"key":"e_1_3_3_146_2","article-title":"Prompt stealing attacks against text-to-image generation models","author":"Shen Xinyue","year":"2023","unstructured":"Xinyue Shen, Yiting Qu, Michael Backes, and Yang Zhang. 2023. Prompt stealing attacks against text-to-image generation models. arXiv preprint arXiv:2302.09923 (2023).","journal-title":"arXiv preprint arXiv:2302.09923"},{"key":"e_1_3_3_147_2","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP51992.2021.00024"},{"key":"e_1_3_3_148_2","article-title":"Using DeepSpeed and Megatron to train Megatron-Turing NLG 530b, a large-scale generative language model","author":"Smith Shaden","year":"2022","unstructured":"Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, et\u00a0al. 2022. Using DeepSpeed and Megatron to train Megatron-Turing NLG 530b, a large-scale generative language model. arXiv preprint arXiv:2201.11990 (2022).","journal-title":"arXiv preprint arXiv:2201.11990"},{"key":"e_1_3_3_149_2","first-page":"2256","volume-title":"International Conference on Machine Learning","author":"Sohl-Dickstein Jascha","year":"2015","unstructured":"Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning. PMLR, 2256\u20132265."},{"key":"e_1_3_3_150_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00586"},{"key":"e_1_3_3_151_2","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417270"},{"key":"e_1_3_3_152_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330885"},{"key":"e_1_3_3_153_2","article-title":"Denoising diffusion implicit models","author":"Song Jiaming","year":"2020","unstructured":"Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020).","journal-title":"arXiv preprint arXiv:2010.02502"},{"key":"e_1_3_3_154_2","article-title":"Generative modeling by estimating gradients of the data distribution","volume":"32","author":"Song Yang","year":"2019","unstructured":"Yang Song and Stefano Ermon. 2019. Generative modeling by estimating gradients of the data distribution. Advances in Neural Information Processing Systems 32 (2019).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_155_2","volume-title":"Snow Crash: A Novel","author":"Stephenson Neal","year":"2003","unstructured":"Neal Stephenson. 2003. Snow Crash: A Novel. Spectra."},{"key":"e_1_3_3_156_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3128646"},{"key":"e_1_3_3_157_2","unstructured":"J. Sun W. Gan H. C. Chao and P. S. Yu. 2022. Metaverse: Survey applications security and opportunities. arXiv preprint arXiv:2210.07990 (2022)."},{"key":"e_1_3_3_158_2","doi-asserted-by":"publisher","DOI":"10.1109\/SECON58729.2023.10287523"},{"key":"e_1_3_3_159_2","doi-asserted-by":"publisher","DOI":"10.1207\/s15516709cog1202_4"},{"key":"e_1_3_3_160_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-04831-9"},{"key":"e_1_3_3_161_2","article-title":"The roadmap of communication and networking in 6G for the metaverse","author":"Tang Fengxiao","year":"2022","unstructured":"Fengxiao Tang, Xuehan Chen, Ming Zhao, and Nei Kato. 2022. The roadmap of communication and networking in 6G for the metaverse. IEEE Wireless Communications (2022).","journal-title":"IEEE Wireless Communications"},{"key":"e_1_3_3_162_2","article-title":"Human motion diffusion model","author":"Tevet Guy","year":"2022","unstructured":"Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H. Bermano. 2022. Human motion diffusion model. arXiv preprint arXiv:2209.14916 (2022).","journal-title":"arXiv preprint arXiv:2209.14916"},{"key":"e_1_3_3_163_2","article-title":"Understanding unintended memorization in federated learning","author":"Thakkar Om","year":"2020","unstructured":"Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, and Fran\u00e7oise Beaufays. 2020. Understanding unintended memorization in federated learning. arXiv preprint arXiv:2006.07490 (2020).","journal-title":"arXiv preprint arXiv:2006.07490"},{"key":"e_1_3_3_164_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01885"},{"key":"e_1_3_3_165_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2014.6736744"},{"key":"e_1_3_3_166_2","article-title":"LaMDA: Language models for dialog applications","author":"Thoppilan Romal","year":"2022","unstructured":"Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et\u00a0al. 2022. LaMDA: Language models for dialog applications. arXiv preprint arXiv:2201.08239 (2022).","journal-title":"arXiv preprint arXiv:2201.08239"},{"key":"e_1_3_3_167_2","doi-asserted-by":"publisher","DOI":"10.1145\/3551636"},{"key":"e_1_3_3_168_2","article-title":"LLaMA: Open and efficient foundation language models","author":"Touvron Hugo","year":"2023","unstructured":"Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth\u00e9e Lacroix, Baptiste Rozi\u00e8re, Naman Goyal, Eric Hambro, Faisal Azhar, et\u00a0al. 2023. LLaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023).","journal-title":"arXiv preprint arXiv:2302.13971"},{"key":"e_1_3_3_169_2","article-title":"LLaMA 2: Open foundation and fine-tuned chat models","author":"Touvron Hugo","year":"2023","unstructured":"Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et\u00a0al. 2023. LLaMA 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023).","journal-title":"arXiv preprint arXiv:2307.09288"},{"key":"e_1_3_3_170_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3072221"},{"key":"e_1_3_3_171_2","article-title":"Attention is all you need","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_172_2","article-title":"The effects of auditory, visual, and cognitive distractions on cybersickness in virtual reality","author":"Venkatakrishnan Rohith","year":"2023","unstructured":"Rohith Venkatakrishnan, Roshan Venkatakrishnan, Balagopal Raveendranath, Dawn M. Sarno, Andrew C. Robb, Wen-Chieh Lin, and Sabarish V. Babu. 2023. The effects of auditory, visual, and cognitive distractions on cybersickness in virtual reality. IEEE Transactions on Visualization and Computer Graphics (2023).","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"e_1_3_3_173_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11845-023-03437-z"},{"key":"e_1_3_3_174_2","article-title":"Poisoning language models during instruction tuning","author":"Wan Alexander","year":"2023","unstructured":"Alexander Wan, Eric Wallace, Sheng Shen, and Dan Klein. 2023. Poisoning language models during instruction tuning. arXiv preprint arXiv:2305.00944 (2023).","journal-title":"arXiv preprint arXiv:2305.00944"},{"key":"e_1_3_3_175_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58592-1_14"},{"key":"e_1_3_3_176_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i12.26701"},{"key":"e_1_3_3_177_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02014"},{"key":"e_1_3_3_178_2","article-title":"A survey on ChatGPT: AI-generated contents, challenges, and solutions","author":"Wang Yuntao","year":"2023","unstructured":"Yuntao Wang, Yanghe Pan, Miao Yan, Zhou Su, and Tom H. Luan. 2023. A survey on ChatGPT: AI-generated contents, challenges, and solutions. arXiv preprint arXiv:2305.18339 (2023).","journal-title":"arXiv preprint arXiv:2305.18339"},{"key":"e_1_3_3_179_2","article-title":"A survey on metaverse: Fundamentals, security, and privacy","author":"Wang Yuntao","year":"2022","unstructured":"Yuntao Wang, Zhou Su, Ning Zhang, Rui Xing, Dongxiao Liu, Tom H. Luan, and Xuemin Shen. 2022. A survey on metaverse: Fundamentals, security, and privacy. IEEE Communications Surveys & Tutorials (2022).","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"e_1_3_3_180_2","article-title":"Adversarial attacks and defenses in machine learning-powered networks: A contemporary survey","volume":"2303","author":"Wang Yulong","year":"2023","unstructured":"Yulong Wang, Tong Sun, Shenghong Li, Xinnan Yuan, Wei Ni, Ekram Hossain, and H. Vincent Poor. 2023. Adversarial attacks and defenses in machine learning-powered networks: A contemporary survey. ArXiv abs\/2303.06302 (2023).","journal-title":"ArXiv"},{"key":"e_1_3_3_181_2","article-title":"Energy-latency attacks to on-device neural networks via sponge poisoning","author":"Wang Zijian","year":"2023","unstructured":"Zijian Wang, Shuo Huang, Yujin Huang, and Helei Cui. 2023. Energy-latency attacks to on-device neural networks via sponge poisoning. arXiv preprint arXiv:2305.03888 (2023).","journal-title":"arXiv preprint arXiv:2305.03888"},{"key":"e_1_3_3_182_2","article-title":"Ethical and social risks of harm from language models","author":"Weidinger Laura","year":"2021","unstructured":"Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, et\u00a0al. 2021. Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021).","journal-title":"arXiv preprint arXiv:2112.04359"},{"key":"e_1_3_3_183_2","doi-asserted-by":"publisher","DOI":"10.22215\/timreview\/1282"},{"key":"e_1_3_3_184_2","article-title":"AI-generated content (AIGC): A survey","author":"Wu Jiayang","year":"2023","unstructured":"Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, and Hong Lin. 2023. AI-generated content (AIGC): A survey. arXiv preprint arXiv:2304.06632 (2023).","journal-title":"arXiv preprint arXiv:2304.06632"},{"key":"e_1_3_3_185_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00701"},{"key":"e_1_3_3_186_2","volume-title":"International Conference on Learning Representations","author":"Xie Chulin","year":"2019","unstructured":"Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li. 2019. DBA: Distributed backdoor attacks against federated learning. In International Conference on Learning Representations."},{"key":"e_1_3_3_187_2","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612704"},{"key":"e_1_3_3_188_2","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3069258"},{"key":"e_1_3_3_189_2","volume-title":"NeurIPS Workshop on Backdoors in Deep Learning-The Good, the Bad, and the Ugly","author":"Yan Jun","year":"2023","unstructured":"Jun Yan, Vikas Yadav, Shiyang Li, Lichang Chen, Zheng Tang, Hai Wang, Vijay Srinivasan, Xiang Ren, and Hongxia Jin. 2023. Backdooring instruction-tuned large language models with virtual prompt injection. In NeurIPS Workshop on Backdoors in Deep Learning-The Good, the Bad, and the Ugly."},{"key":"e_1_3_3_190_2","first-page":"39299","volume-title":"International Conference on Machine Learning","author":"Yang Ziqing","year":"2023","unstructured":"Ziqing Yang, Xinlei He, Zheng Li, Michael Backes, Mathias Humbert, Pascal Berrang, and Yang Zhang. 2023. Data poisoning attacks against multimodal encoders. In International Conference on Machine Learning. PMLR, 39299\u201339313."},{"key":"e_1_3_3_191_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583306"},{"key":"e_1_3_3_192_2","article-title":"LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop","author":"Yu Fisher","year":"2015","unstructured":"Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao. 2015. LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365 (2015).","journal-title":"arXiv preprint arXiv:1506.03365"},{"key":"e_1_3_3_193_2","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2020.24178"},{"key":"e_1_3_3_194_2","article-title":"Pseudo label-guided model inversion attack via conditional generative adversarial network","author":"Yuan Xiaojian","year":"2023","unstructured":"Xiaojian Yuan, Kejiang Chen, Jie Zhang, Weiming Zhang, Nenghai Yu, and Yang Zhang. 2023. Pseudo label-guided model inversion attack via conditional generative adversarial network. arXiv preprint arXiv:2302.09814 (2023).","journal-title":"arXiv preprint arXiv:2302.09814"},{"key":"e_1_3_3_195_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01467"},{"key":"e_1_3_3_196_2","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417880"},{"key":"e_1_3_3_197_2","article-title":"GLM-130B: An open bilingual pre-trained model","author":"Zeng Aohan","year":"2022","unstructured":"Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, et\u00a0al. 2022. GLM-130B: An open bilingual pre-trained model. arXiv preprint arXiv:2210.02414 (2022).","journal-title":"arXiv preprint arXiv:2210.02414"},{"key":"e_1_3_3_198_2","article-title":"CommonScenes: Generating commonsense 3D indoor scenes with scene graphs","volume":"36","author":"Zhai Guangyao","year":"2024","unstructured":"Guangyao Zhai, Evin P\u0131nar \u00d6rnek, Shun-Cheng Wu, Yan Di, Federico Tombari, Nassir Navab, and Benjamin Busam. 2024. CommonScenes: Generating commonsense 3D indoor scenes with scene graphs. Advances in Neural Information Processing Systems 36 (2024).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_199_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106775"},{"key":"e_1_3_3_200_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3023126"},{"key":"e_1_3_3_201_2","doi-asserted-by":"publisher","DOI":"10.1109\/TrustCom\/BigDataSE.2019.00057"},{"key":"e_1_3_3_202_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148790"},{"key":"e_1_3_3_203_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"e_1_3_3_204_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11833"},{"key":"e_1_3_3_205_2","first-page":"201","volume-title":"Learning for Dynamics and Control","author":"Zhang Xuezhou","year":"2020","unstructured":"Xuezhou Zhang, Xiaojin Zhu, and Laurent Lessard. 2020. Online data poisoning attacks. In Learning for Dynamics and Control. PMLR, 201\u2013210."},{"key":"e_1_3_3_206_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00033"},{"key":"e_1_3_3_207_2","doi-asserted-by":"publisher","DOI":"10.1145\/3474369.3486863"},{"key":"e_1_3_3_208_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"e_1_3_3_209_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.visinf.2022.03.002"},{"key":"e_1_3_3_210_2","article-title":"Property inference attacks against GANs","author":"Zhou Junhao","year":"2021","unstructured":"Junhao Zhou, Yufei Chen, Chao Shen, and Yang Zhang. 2021. 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