{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:28:09Z","timestamp":1771514889035,"version":"3.50.1"},"reference-count":79,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:00:00Z","timestamp":1656374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["62121002, 62022076, U1936210, and 62032006"],"award-info":[{"award-number":["62121002, 62022076, U1936210, and 62032006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["2020M682035"],"award-info":[{"award-number":["2020M682035"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Hefei Postdoctoral Research Activities Foundation","award":["BSH202101"],"award-info":[{"award-number":["BSH202101"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["WK3480000011"],"award-info":[{"award-number":["WK3480000011"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2022,8,31]]},"abstract":"<jats:p>The spread of face forgery videos is a serious threat to information credibility, calling for effective detection algorithms to identify them. Most existing methods have assumed a shared or centralized training set. However, in practice, data may be distributed on devices of different enterprises that cannot be centralized to share due to security and privacy restrictions. In this article, we propose a Federated Learning face forgery detection framework to train a global model collaboratively while keeping data on local devices. In order to make the detection model more robust, we propose a novel Inconsistency-Capture module (ICM) to capture the dynamic inconsistencies between adjacent frames of face forgery videos. The ICM contains two parallel branches. The first branch takes the whole face of adjacent frames as input to calculate a global inconsistency representation. The second branch focuses only on the inter-frame variation of critical regions to capture the local inconsistency. To the best of our knowledge, this is the first work to apply federated learning to face forgery video detection, which is trained with decentralized data. Extensive experiments show that the proposed framework achieves competitive performance compared with existing methods that are trained with centralized data, with higher-level security and privacy guarantee.<\/jats:p>","DOI":"10.1145\/3501814","type":"journal-article","created":{"date-parts":[[2022,2,4]],"date-time":"2022-02-04T22:33:18Z","timestamp":1644013998000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Dynamic-Aware Federated Learning for Face Forgery Video Detection"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2936-0996","authenticated-orcid":false,"given":"Ziheng","family":"Hu","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Hongtao","family":"Xie","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Lingyun","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China"}]},{"given":"Xingyu","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Zhihua","family":"Shang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Yongdong","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2022,6,28]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1","volume-title":"IEEE International Workshop on Information Forensics and Security (WIFS\u201918)","author":"Afchar Darius","year":"2018","unstructured":"Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen. 2018. MesoNet: A compact facial video forgery detection network. In IEEE International Workshop on Information Forensics and Security (WIFS\u201918), Hong Kong, China, December 11\u201313, 2018. IEEE, 1\u20137."},{"key":"e_1_3_1_3_2","unstructured":"Shruti Agarwal Hany Farid Yuming Gu Mingming He Koki Nagano and Hao Li. 2019. Protecting world leaders against deep fakes. In IEEE Conference on Computer Vision and Pattern RecognitionWorkshops (CVPR\u201919) Long Beach CA USA June 16-20 2019. IEEE 38\u201345."},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00152"},{"key":"e_1_3_1_5_2","unstructured":"Jianmin Bao Dong Chen Fang Wen Houqiang Li and Gang Hua. 2018. Towards open-set identity preserving face synthesis. In 2018 IEEE Conference on Computer Vision and Pattern Recognition CVPR 2018 Salt Lake City UT USA June 18-22 2018 . IEEE 6713\u20136722."},{"key":"e_1_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Belhassen Bayar and Matthew C. Stamm. 2016. A deep learning approach to universal image manipulation detection using a new convolutional layer. In Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security IH&MMSec 2016 Vigo Galicia Spain June 20-22 2016. ACM 5\u201310.","DOI":"10.1145\/2909827.2930786"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.05.006"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00916"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3082031.3083247"},{"key":"e_1_3_1_11_2","article-title":"ForensicTransfer: Weakly-supervised domain adaptation for forgery detection","author":"Cozzolino Davide","year":"2018","unstructured":"Davide Cozzolino, Justus Thies, Andreas R\u00f6ssler, Christian Riess, Matthias Nie\u00dfner, and Luisa Verdoliva. 2018. ForensicTransfer: Weakly-supervised domain adaptation for forgery detection. arXiv:1812.02510.","journal-title":"arXiv:1812.02510."},{"key":"e_1_3_1_12_2","unstructured":"DeepFakes. 2019. Retrieved February 14 2022 from http:\/\/www.github.com\/deepfakes\/faceswap."},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00791"},{"key":"e_1_3_1_15_2","unstructured":"FaceSwap. 2019. Retrieved February 14 2022 from www.github.com\/MarekKowalski\/FaceSwap."},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2012.2190402"},{"key":"e_1_3_1_17_2","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow Ian","year":"2014","unstructured":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advances in Neural Information Processing Systems 27 (2014), 2672\u20132680.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2018.8639163"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00500"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2019.2916751"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3105617"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3363818"},{"key":"e_1_3_1_24_2","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu Tzu-Ming Harry","year":"2019","unstructured":"Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. 2019. Measuring the effects of non-identical data distribution for federated visual classification. arXiv:1909.06335.","journal-title":"arXiv:1909.06335."},{"key":"e_1_3_1_25_2","article-title":"FakeRetouch: Evading deepfakes detection via the guidance of deliberate noise","author":"Huang Yihao","year":"2020","unstructured":"Yihao Huang, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Lei Ma, Weikai Miao, Yang Liu, and Geguang Pu. 2020. FakeRetouch: Evading deepfakes detection via the guidance of deliberate noise. arXiv:2009.09213.","journal-title":"arXiv:2009.09213."},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3418283"},{"key":"e_1_3_1_27_2","article-title":"Progressive growing of GANs for improved quality, stability, and variation","author":"Karras Tero","year":"2017","unstructured":"Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2017. Progressive growing of GANs for improved quality, stability, and variation. arXiv:1710.10196.","journal-title":"arXiv:1710.10196."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.397"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00639"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"e_1_3_1_32_2","first-page":"133","volume-title":"International Workshop on Machine Learning in Medical Imaging","author":"Li Wenqi","year":"2019","unstructured":"Wenqi Li, Fausto Milletar\u00ec, Daguang Xu, Nicola Rieke, Jonny Hancox, Wentao Zhu, Maximilian Baust, Yan Cheng, S\u00e9bastien Ourselin, M. Jorge Cardoso, et\u00a0al. 2019. Privacy-preserving federated brain tumour segmentation. In International Workshop on Machine Learning in Medical Imaging. Springer, 133\u2013141."},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3414034"},{"key":"e_1_3_1_34_2","volume-title":"IEEE International Workshop on Information Forensics and Security (WIFS\u201918)","author":"Li Yuezun","year":"2018","unstructured":"Yuezun Li, Ming-Ching Chang, and Siwei Lyu. 2018. In ictu oculi: Exposing AI created fake videos by detecting eye blinking. In IEEE International Workshop on Information Forensics and Security (WIFS\u201918), Hong Kong, China, December 11\u201313, 2018. IEEE, 1\u20137."},{"key":"e_1_3_1_35_2","article-title":"Exposing deepfake videos by detecting face warping artifacts","author":"Li Yuezun","year":"2018","unstructured":"Yuezun Li and Siwei Lyu. 2018. Exposing deepfake videos by detecting face warping artifacts. arXiv:1811.00656","journal-title":"arXiv:1811.00656"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"e_1_3_1_37_2","article-title":"Hiding faces in plain sight: Disrupting AI face synthesis with adversarial perturbations","author":"Li Yuezun","year":"2019","unstructured":"Yuezun Li, Xin Yang, Baoyuan Wu, and Siwei Lyu. 2019. Hiding faces in plain sight: Disrupting AI face synthesis with adversarial perturbations. arXiv:1906.09288.","journal-title":"arXiv:1906.09288."},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.2984569"},{"key":"e_1_3_1_39_2","article-title":"Recent advances in monocular 2D and 3D human pose estimation: A deep learning perspective","author":"Liu Wu","year":"2021","unstructured":"Wu Liu, Qian Bao, Yu Sun, and Tao Mei. 2021. Recent advances in monocular 2D and 3D human pose estimation: A deep learning perspective. arXiv:2104.11536.","journal-title":"arXiv:2104.11536."},{"key":"e_1_3_1_40_2","article-title":"Real-world image datasets for federated learning","author":"Luo Jiahuan","year":"2019","unstructured":"Jiahuan Luo, Xueyang Wu, Yun Luo, Anbu Huang, Yunfeng Huang, Yang Liu, and Qiang Yang. 2019. Real-world image datasets for federated learning. arXiv:1910.11089.","journal-title":"arXiv:1910.11089."},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01605"},{"key":"e_1_3_1_42_2","doi-asserted-by":"crossref","unstructured":"Iacopo Masi Aditya Killekar Royston Marian Mascarenhas Shenoy Pratik Gurudatt and Wael AbdAlmageed. 2020. Two-branch recurrent network for isolating deepfakes in videos. In Computer Vision - ECCV 2020-16th European Conference Glasgow UK August 23\u201328 2020 Proceedings Part VII (Lecture Notes in Computer Science) Vol. 12352. Springer 667\u2013684.","DOI":"10.1007\/978-3-030-58571-6_39"},{"key":"e_1_3_1_43_2","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Articial Intelligence and Statistics AISTATS 2017 20-22 April 2017 Fort Lauderdale FL USA (Proceedings of Machine Learning Research) Vol. 54. PMLR 1273\u20131282."},{"key":"e_1_3_1_44_2","article-title":"Multi-task learning for detecting and segmenting manipulated facial images and videos","author":"Nguyen Huy H.","year":"2019","unstructured":"Huy H. Nguyen, Fuming Fang, Junichi Yamagishi, and Isao Echizen. 2019. Multi-task learning for detecting and segmenting manipulated facial images and videos. arXiv:1906.06876.","journal-title":"arXiv:1906.06876."},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682602"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482252"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447585"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2021.3057082"},{"key":"e_1_3_1_49_2","article-title":"Invertible conditional GANs for image editing","author":"Perarnau Guim","year":"2016","unstructured":"Guim Perarnau, Joost Van De Weijer, Bogdan Raducanu, and Jose M. \u00c1lvarez. 2016. Invertible conditional GANs for image editing. arXiv:1611.06355.","journal-title":"arXiv:1611.06355."},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413707"},{"key":"e_1_3_1_51_2","doi-asserted-by":"crossref","unstructured":"Yuyang Qian Guojun Yin Lu Sheng Zixuan Chen and Jing Shao. 2020. Thinking in frequency: Face forgery detection by mining frequency-aware clues. In Proceedings of the 16th European Conference Glasgow UK August 23\u201328 2020 Proceedings Part XII Computer Vision - ECCV 2020. Lecture Notes in Computer Science Vol. 12357. Springer 86\u2013103.","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"e_1_3_1_52_2","doi-asserted-by":"crossref","unstructured":"Nicolas Rahmouni Vincent Nozick Junichi Yamagishi and Isao Echizen. 2017. Distinguishing computer graphics from natural images using convolution neural networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops CVPR Workshops 2017 Honolulu HI USA July 21\u201326 2017 . IEEE 1822\u20131830.","DOI":"10.1109\/WIFS.2017.8267647"},{"key":"e_1_3_1_53_2","unstructured":"Kiran Raja Sushma Venkatesh R. B. Christoph Busch et\u00a0al. 2017. Transferable deep-CNN features for detecting digital and print-scanned morphed face images. In 2017 IEEE Workshop on Information Forensics and Security WIFS 2017 Rennes France December 4\u20137 2017. IEEE 1\u20136."},{"key":"e_1_3_1_54_2","unstructured":"Alexander Ratner Dan Alistarh Gustavo Alonso David G. Andersen Peter Bailis Sarah Bird Nicholas Carlini Bryan Catanzaro Eric Chung Bill Dally et\u00a0al. 2019. SysML: The new frontier of machine learning systems. arXiv preprint 1904.03257 (2019)."},{"key":"e_1_3_1_55_2","unstructured":"Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie and Ramtin Pedarsani. 2020. FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization. In The 23rd International Conference on Articial Intelligence and Statistics AISTATS 2020 26\u201328 August 2020 Online [Palermo Sicily Italy] (Proceedings of Machine Learning Research) Vol. 108. PMLR 2021\u20132031."},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00009"},{"key":"e_1_3_1_57_2","unstructured":"Ekraam Sabir Jiaxin Cheng Ayush Jaiswal Wael AbdAlmageed Iacopo Masi and Prem Natarajan. 2019. Recurrent convolutional strategies for face manipulation detection in videos. In IEEE Conference on Computer Vision and Pattern Recognition Workshops CVPR Workshops 2019 Long Beach CA USA June 16\u201320 2019. IEEE 80\u201387."},{"key":"e_1_3_1_58_2","article-title":"Dynamic routing between capsules","author":"Sabour Sara","year":"2017","unstructured":"Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton. 2017. Dynamic routing between capsules. arXiv:1710.09829.","journal-title":"arXiv:1710.09829."},{"key":"e_1_3_1_59_2","article-title":"On the convergence of federated optimization in heterogeneous networks","volume":"3","author":"Sahu Anit Kumar","year":"2018","unstructured":"Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith. 2018. On the convergence of federated optimization in heterogeneous networks. arXiv:1812.06127 3.","journal-title":"arXiv:1812.06127"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107950"},{"key":"e_1_3_1_62_2","article-title":"Complement face forensic detection and localization with faciallandmarks","author":"Songsri-in Kritaphat","year":"2019","unstructured":"Kritaphat Songsri-in and Stefanos Zafeiriou. 2019. Complement face forensic detection and localization with faciallandmarks. arXiv:1910.05455.","journal-title":"arXiv:1910.05455."},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3323035"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/2929464.2929475"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00202"},{"issue":"11","key":"e_1_3_1_66_2","article-title":"Visualizing data using t-SNE.","volume":"9","author":"Maaten Laurens Van der","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 11 (2008), 2579\u20132605.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_67_2","article-title":"Federated learning with matched averaging","author":"Wang Hongyi","year":"2020","unstructured":"Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni. 2020. Federated learning with matched averaging. arXiv:2002.06440.","journal-title":"arXiv:2002.06440."},{"key":"e_1_3_1_68_2","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","author":"Wang Jianyu","year":"2020","unstructured":"Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H. Vincent Poor. 2020. Tackling the objective inconsistency problem in heterogeneous federated optimization. arXiv:2007.07481.","journal-title":"arXiv:2007.07481."},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2995290"},{"key":"e_1_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01177"},{"key":"e_1_3_1_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.2988575"},{"key":"e_1_3_1_72_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3414012"},{"key":"e_1_3_1_73_2","article-title":"An overview of facial micro-expression analysis: Data, methodology and challenge","author":"Xie Hong-Xia","year":"2020","unstructured":"Hong-Xia Xie, Ling Lo, Hong-Han Shuai, and Wen-Huang Cheng. 2020. An overview of facial micro-expression analysis: Data, methodology and challenge. arXiv:2012.11307.","journal-title":"arXiv:2012.11307."},{"key":"e_1_3_1_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3113708"},{"key":"e_1_3_1_75_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2990082"},{"key":"e_1_3_1_76_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683164"},{"key":"e_1_3_1_77_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.2973374"},{"key":"e_1_3_1_78_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00089"},{"key":"e_1_3_1_79_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.229"},{"key":"e_1_3_1_80_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3501814","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3501814","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:48Z","timestamp":1750183788000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3501814"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,28]]},"references-count":79,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,8,31]]}},"alternative-id":["10.1145\/3501814"],"URL":"https:\/\/doi.org\/10.1145\/3501814","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,28]]},"assertion":[{"value":"2021-04-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-06-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}