{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T22:21:54Z","timestamp":1766442114694,"version":"3.48.0"},"publisher-location":"New York, NY, USA","reference-count":86,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,19]]},"DOI":"10.1145\/3719027.3744868","type":"proceedings-article","created":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T23:33:16Z","timestamp":1763854396000},"page":"3117-3131","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["What's Pulling the Strings? Evaluating Integrity and Attribution in AI Training and Inference through Concept Shift"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8428-884X","authenticated-orcid":false,"given":"Jiamin","family":"Chang","sequence":"first","affiliation":[{"name":"University of New South Wales and CSIRO's Data61, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8235-6753","authenticated-orcid":false,"given":"Haoyang","family":"Li","sequence":"additional","affiliation":[{"name":"Macquarie University, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3488-7004","authenticated-orcid":false,"given":"Hammond","family":"Pearce","sequence":"additional","affiliation":[{"name":"University of New South Wales, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5404-8550","authenticated-orcid":false,"given":"Ruoxi","family":"Sun","sequence":"additional","affiliation":[{"name":"CSIRO's Data61, Adelaide, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4883-7267","authenticated-orcid":false,"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Champaign and Urbana, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5411-5039","authenticated-orcid":false,"given":"Minhui","family":"Xue","sequence":"additional","affiliation":[{"name":"CSIRO's Data61, Adelaide, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,22]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Stability AI. 2023. Stable Diffusion 2.1. https:\/\/huggingface.co\/stabilityai\/stable-diffusion-2-1"},{"key":"e_1_3_2_1_2_1","volume-title":"Sid Ahmed Fezza, and Olivier Deforges","author":"Aldahdooh Ahmed","year":"2022","unstructured":"Ahmed Aldahdooh, Wassim Hamidouche, Sid Ahmed Fezza, and Olivier Deforges. 2022. Adversarial example detection for DNN models: A review and experimental comparison. Artificial Intelligence Review (2022)."},{"key":"e_1_3_2_1_3_1","unstructured":"Aliyun. 2024. TongYiWanXiang. https:\/\/tongyi.aliyun.com\/wanxiang\/"},{"key":"e_1_3_2_1_4_1","volume-title":"Detecting language model attacks with perplexity. arXiv preprint arXiv:2308.14132","author":"Alon Gabriel","year":"2023","unstructured":"Gabriel Alon and Michael Kamfonas. 2023. Detecting language model attacks with perplexity. arXiv preprint arXiv:2308.14132 (2023)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833659"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1907375117"},{"key":"e_1_3_2_1_7_1","volume-title":"On hate scaling laws for data-swamps. arXiv preprint arXiv:2306.13141","author":"Birhane Abeba","year":"2023","unstructured":"Abeba Birhane, Vinay Prabhu, Sang Han, and Vishnu Naresh Boddeti. 2023. On hate scaling laws for data-swamps. arXiv preprint arXiv:2306.13141 (2023)."},{"key":"e_1_3_2_1_8_1","unstructured":"Black Forest Labs. 2024. FLUX.1. https:\/\/huggingface.co\/black-forest-labs\/FLUX.1-dev"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"e_1_3_2_1_10_1","volume-title":"On evaluating adversarial robustness. arXiv preprint arXiv:1902.06705","author":"Carlini Nicholas","year":"2019","unstructured":"Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, Aleksander Madry, and Alexey Kurakin. 2019. On evaluating adversarial robustness. arXiv preprint arXiv:1902.06705 (2019)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833649"},{"key":"e_1_3_2_1_12_1","first-page":"5253","volume-title":"32nd USENIX Security Symposium (USENIX Security 23)","author":"Carlini Nicolas","year":"2023","unstructured":"Nicolas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ippolito, and Eric Wallace. 2023. Extracting training data from diffusion models. In 32nd USENIX Security Symposium (USENIX Security 23). 5253-5270."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140444"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00356"},{"key":"e_1_3_2_1_16_1","unstructured":"CompVis. 2022. Stable Diffusion 1.4. https:\/\/huggingface.co\/CompVis\/stable-diffusion-v1-4"},{"key":"e_1_3_2_1_17_1","unstructured":"CompVis. 2023. Stable Diffusion XL. https:\/\/huggingface.co\/CompVis\/stable-diffusion-xl-base-1.0"},{"key":"e_1_3_2_1_18_1","unstructured":"CompVis. 2024. Stable Diffusion 3.5. https:\/\/huggingface.co\/CompVis\/stable-diffusion-3.5-large"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988157"},{"key":"e_1_3_2_1_20_1","volume-title":"International Conference on Learning Representations.","author":"Dosovitskiy Alexey","year":"2021","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01763"},{"key":"e_1_3_2_1_22_1","unstructured":"European Parliamentary Research Service. 2025. Algorithmic discrimination under the AI Act and the GDPR. https:\/\/www.europarl.europa.eu\/thinktank\/en\/document\/EPRS_ATA(2025)769509."},{"key":"e_1_3_2_1_23_1","volume-title":"Explaining and Harnessing Adversarial Examples. In International Conference on Learning Representations, Yoshua Bengio and Yann LeCun (Eds.).","author":"Goodfellow Ian J.","year":"2015","unstructured":"Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015. Explaining and Harnessing Adversarial Examples. In International Conference on Learning Representations, Yoshua Bengio and Yann LeCun (Eds.)."},{"key":"e_1_3_2_1_24_1","volume-title":"First Conference on Language Modeling.","author":"He Luxi","year":"2024","unstructured":"Luxi He, Mengzhou Xia, and Peter Henderson. 2024. What is in your safe data? Identifying benign data that breaks safety. In First Conference on Language Modeling."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2011.02.003"},{"key":"e_1_3_2_1_26_1","unstructured":"ISO\/IEC JTC 1\/SC 42. 2020. ISO\/IEC TR 24028:2020 Overview of trustworthiness in artificial intelligence. https:\/\/www.iso.org\/standard\/77608.html"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3363201"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3491209"},{"key":"e_1_3_2_1_29_1","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Ming-Wei Chang Jacob Devlin","year":"2019","unstructured":"Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT. 4171-4186.","journal-title":"Proceedings of NAACL-HLT."},{"key":"e_1_3_2_1_30_1","unstructured":"Douwe Kiela Hamed Firooz Aravind Mohan Vedanuj Goswami Amanpreet Singh Casey A Fitzpatrick Peter Bull Greg Lipstein Tony Nelli Ron Zhu et al. 2021. The hateful memes challenge: Competition report. In NeurIPS 2020 Competition and Demonstration Track. 344-360."},{"key":"e_1_3_2_1_31_1","volume-title":"International Conference on Machine Learning.","author":"Kim Been","year":"2018","unstructured":"Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory sayres. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV). In International Conference on Machine Learning."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0265723"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-016-0981-7"},{"key":"e_1_3_2_1_34_1","unstructured":"Alex Krizhevsky and Geoffrey Hinton. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_36_1","first-page":"9694","article-title":"Align before fuse: Vision and language representation learning with momentum distillation","volume":"34","author":"Li Junnan","year":"2021","unstructured":"Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. 2021. Align before fuse: Vision and language representation learning with momentum distillation. Advances in Neural Information Processing Systems, Vol. 34 (2021), 9694-9705.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.500"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179303"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460120.3484575"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3546872"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP54263.2024.00120"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560684"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00016"},{"key":"e_1_3_2_1_46_1","volume-title":"Indiscriminate Data Poisoning Attacks on Neural Networks. Transactions on Machine Learning Research","author":"Lu Yiwei","year":"2022","unstructured":"Yiwei Lu, Gautam Kamath, and Yaoliang Yu. 2022. Indiscriminate Data Poisoning Attacks on Neural Networks. Transactions on Machine Learning Research (2022)."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2019.23415"},{"key":"e_1_3_2_1_48_1","volume-title":"International Conference on Learning Representations.","author":"Ma Xingjun","year":"2018","unstructured":"Xingjun Ma, Bo Li, Yisen Wang, Sarah M Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E Houle, and James Bailey. 2018. Characterizing adversarial subspaces using local intrinsic dimensionality. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_49_1","volume-title":"International Conference on Learning Representations.","author":"Madry Aleksander","year":"2017","unstructured":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134057"},{"key":"e_1_3_2_1_51_1","unstructured":"Midjourney. 2024. Midjourney. https:\/\/www.midjourney.com\/explore?tab=top"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"e_1_3_2_1_53_1","volume-title":"Vilio: State-of-the-art visio-linguistic models applied to hateful memes. arXiv preprint arXiv:2012.07788","author":"Muennighoff Niklas","year":"2020","unstructured":"Niklas Muennighoff. 2020. Vilio: State-of-the-art visio-linguistic models applied to hateful memes. arXiv preprint arXiv:2012.07788 (2020)."},{"key":"e_1_3_2_1_54_1","unstructured":"OpenAI. 2024a. OpenAI Dall-E 2. https:\/\/openai.com\/index\/dall-e-2"},{"key":"e_1_3_2_1_55_1","unstructured":"OpenAI. 2024b. OpenAI Dall-E 3. https:\/\/openai.com\/index\/dall-e-3"},{"key":"e_1_3_2_1_56_1","volume-title":"Advances in Neural Information Processing Systems","volume":"24","author":"Ordonez Vicente","year":"2011","unstructured":"Vicente Ordonez, Girish Kulkarni, and Tamara Berg. 2011. Im2text: Describing images using 1 million captioned photographs. Advances in Neural Information Processing Systems, Vol. 24 (2011)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2016.36"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2018.00035"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.303"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179315"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3576915.3616679"},{"key":"e_1_3_2_1_62_1","volume-title":"International Conference on Machine Learning. 8748-8763","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al., 2021. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning. 8748-8763."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1999.10485670"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02155"},{"key":"e_1_3_2_1_65_1","volume-title":"AI for Social Good Workshop at NeurIPS","author":"Sabat Benet Oriol","year":"2019","unstructured":"Benet Oriol Sabat, Cristian Canton Ferrer, and Xavier Giro-i Nieto. 2019. Hate speech in pixels: Detection of offensive memes towards automatic moderation. In AI for Social Good Workshop at NeurIPS 2019."},{"key":"e_1_3_2_1_66_1","volume-title":"Updates-leak: Data set inference and reconstruction attacks in online learning. In 29th USENIX security symposium (USENIX Sec' 20). 1291-1308.","author":"Salem Ahmed","year":"2020","unstructured":"Ahmed Salem, Apratim Bhattacharya, Michael Backes, Mario Fritz, and Yang Zhang. 2020. Updates-leak: Data set inference and reconstruction attacks in online learning. In 29th USENIX security symposium (USENIX Sec' 20). 1291-1308."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2019.23119"},{"key":"e_1_3_2_1_68_1","volume-title":"Certified Safe: A Schematic for Approval Regulation of Frontier AI. arXiv preprint arXiv:2408.06210","author":"Salvador Cole","year":"2024","unstructured":"Cole Salvador. 2024. Certified Safe: A Schematic for Approval Regulation of Frontier AI. arXiv preprint arXiv:2408.06210 (2024)."},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976601750264965"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01228-7"},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP54263.2024.00207"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1238"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"e_1_3_2_1_74_1","volume-title":"Deep neural rejection against adversarial examples. EURASIP Journal on Information Security","author":"Sotgiu Angelo","year":"2020","unstructured":"Angelo Sotgiu, Ambra Demontis, Marco Melis, Battista Biggio, Giorgio Fumera, Xiaoyi Feng, and Fabio Roli. 2020. Deep neural rejection against adversarial examples. EURASIP Journal on Information Security (2020)."},{"key":"e_1_3_2_1_75_1","volume-title":"SoK: Unifying cybersecurity and cybersafety of multimodal foundation models with an information theory approach. arXiv preprint arXiv:2411.11195","author":"Sun Ruoxi","year":"2024","unstructured":"Ruoxi Sun, Jiamin Chang, Hammond Pearce, Chaowei Xiao, Bo Li, Qi Wu, Surya Nepal, and Minhui Xue. 2024. SoK: Unifying cybersecurity and cybersafety of multimodal foundation models with an information theory approach. arXiv preprint arXiv:2411.11195 (2024)."},{"key":"e_1_3_2_1_76_1","volume-title":"Proceedings of the 2024 USENIX Security Symposium.","author":"Wang Shuo","year":"2024","unstructured":"Shuo Wang, Hongsheng Hu, Jiamin Chang, Benjamin Zi Hao Zhao, Qi Alfred Chen, and Minhui Xue. 2024. DNN-GP: Diagnosing and mitigating model's faults using latent concepts. In Proceedings of the 2024 USENIX Security Symposium."},{"key":"e_1_3_2_1_77_1","volume-title":"System-level defense against indirect prompt injection attacks: An information flow control perspective. arXiv preprint arXiv:2409.19091","author":"Wu Fangzhou","year":"2024","unstructured":"Fangzhou Wu, Ethan Cecchetti, and Chaowei Xiao. 2024a. System-level defense against indirect prompt injection attacks: An information flow control perspective. arXiv preprint arXiv:2409.19091 (2024)."},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/3658644.3690288"},{"key":"e_1_3_2_1_79_1","volume-title":"Visual entailment: A novel task for fine-grained image understanding. arXiv preprint arXiv:1901.06706","author":"Xie Ning","year":"2019","unstructured":"Ning Xie, Farley Lai, Derek Doran, and Asim Kadav. 2019. Visual entailment: A novel task for fine-grained image understanding. arXiv preprint arXiv:1901.06706 (2019)."},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_5"},{"key":"e_1_3_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"e_1_3_2_1_82_1","volume-title":"International Conference on Machine Learning.","author":"Zarifzadeh Sajjad","year":"2024","unstructured":"Sajjad Zarifzadeh, Philippe Liu, and Reza Shokri. 2024. Low-cost high-power membership inference attacks. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612108"},{"key":"e_1_3_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547801"},{"key":"e_1_3_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00033"},{"key":"e_1_3_2_1_86_1","volume-title":"Enhance multimodal transformer with external label and in-domain pretrain: Hateful meme challenge winning solution. arXiv preprint arXiv:2012.08290","author":"Zhu Ron","year":"2020","unstructured":"Ron Zhu. 2020. Enhance multimodal transformer with external label and in-domain pretrain: Hateful meme challenge winning solution. arXiv preprint arXiv:2012.08290 (2020)."}],"event":{"name":"CCS '25: ACM SIGSAC Conference on Computer and Communications Security","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control"],"location":"Taipei Taiwan","acronym":"CCS '25"},"container-title":["Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3719027.3744868","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T22:17:22Z","timestamp":1766441842000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3719027.3744868"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,19]]},"references-count":86,"alternative-id":["10.1145\/3719027.3744868","10.1145\/3719027"],"URL":"https:\/\/doi.org\/10.1145\/3719027.3744868","relation":{},"subject":[],"published":{"date-parts":[[2025,11,19]]},"assertion":[{"value":"2025-11-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}