{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:30:18Z","timestamp":1792787418071,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876134, U1836202, 62076187, 62172303, 61872181"],"award-info":[{"award-number":["61876134, U1836202, 62076187, 62172303, 61872181"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2021CFB089"],"award-info":[{"award-number":["2021CFB089"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness","award":["HNTS2022004"],"award-info":[{"award-number":["HNTS2022004"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2042021kf1030"],"award-info":[{"award-number":["2042021kf1030"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFB3100700"],"award-info":[{"award-number":["2021YFB3100700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20200418, BE2020106"],"award-info":[{"award-number":["BK20200418, BE2020106"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20210324134408023"],"award-info":[{"award-number":["JCYJ20210324134408023"]}]},{"name":"Fellowship of China National Postdoctoral Program for Innovative Talents","award":["BX2021229"],"award-info":[{"award-number":["BX2021229"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3503161.3548390","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T11:43:12Z","timestamp":1665402192000},"page":"1808-1818","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Rethinking the Vulnerability of DNN Watermarking"],"prefix":"10.1145","author":[{"given":"Run","family":"Wang","sequence":"first","affiliation":[{"name":"Wuhan University, China; Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, China, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoxuan","family":"Li","sequence":"additional","affiliation":[{"name":"Wuhan University, China; Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, China, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingzhou","family":"Mu","sequence":"additional","affiliation":[{"name":"Wuhan University, China; Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, China, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jixing","family":"Ren","sequence":"additional","affiliation":[{"name":"Wuhan University, China; Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, China, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shangwei","family":"Guo","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[{"name":"Fudan Development Institute, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liming","family":"Fang","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Wuhan University, China; Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, China, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lina","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Wuhan University, China; Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, China; Zhengzhou Xinda Institute of Advanced Technology, Zhengzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"2022. Keras-vggface. https:\/\/github.com\/rcmalli\/keras-vggface.  2022. Keras-vggface. https:\/\/github.com\/rcmalli\/keras-vggface."},{"key":"e_1_3_2_2_2_1","volume-title":"27th {USENIX} Security Symposium ({USENIX} Security 18). 1615--1631.","author":"Adi Yossi","unstructured":"Yossi Adi , Carsten Baum , Moustapha Cisse , Benny Pinkas , and Joseph Keshet . 2018. Turning your weakness into a strength: Watermarking deep neural networks by backdooring . In 27th {USENIX} Security Symposium ({USENIX} Security 18). 1615--1631. Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet. 2018. Turning your weakness into a strength: Watermarking deep neural networks by backdooring. In 27th {USENIX} Security Symposium ({USENIX} Security 18). 1615--1631."},{"key":"e_1_3_2_2_3_1","volume-title":"Neural network laundering: Removing black-box backdoor watermarks from deep neural networks. arXiv preprint arXiv:2004.11368","author":"Aiken William","year":"2020","unstructured":"William Aiken , Hyoungshick Kim , and Simon Woo . 2020. Neural network laundering: Removing black-box backdoor watermarks from deep neural networks. arXiv preprint arXiv:2004.11368 ( 2020 ). William Aiken, Hyoungshick Kim, and Simon Woo. 2020. Neural network laundering: Removing black-box backdoor watermarks from deep neural networks. arXiv preprint arXiv:2004.11368 (2020)."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102277"},{"key":"e_1_3_2_2_5_1","volume-title":"30th USENIX Security Symposium (USENIX Security 21)","author":"Bagdasaryan Eugene","year":"2021","unstructured":"Eugene Bagdasaryan and Vitaly Shmatikov . 2021 . Blind backdoors in deep learning models . In 30th USENIX Security Symposium (USENIX Security 21) . 1505--1521. Eugene Bagdasaryan and Vitaly Shmatikov. 2021. Blind backdoors in deep learning models. In 30th USENIX Security Symposium (USENIX Security 21). 1505--1521."},{"key":"e_1_3_2_2_6_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 2938--2948","author":"Bagdasaryan Eugene","year":"2020","unstructured":"Eugene Bagdasaryan , Andreas Veit , Yiqing Hua , Deborah Estrin , and Vitaly Shmatikov . 2020 . How to backdoor federated learning . In International Conference on Artificial Intelligence and Statistics. PMLR, 2938--2948 . Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2020. How to backdoor federated learning. In International Conference on Artificial Intelligence and Statistics. PMLR, 2938--2948."},{"key":"e_1_3_2_2_7_1","unstructured":"Tom B Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell etal 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165 (2020).  Tom B Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165 (2020)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140444"},{"key":"e_1_3_2_2_9_1","volume-title":"Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp)","author":"Carlini Nicholas","unstructured":"Nicholas Carlini and David Wagner . 2017b. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp) . IEEE , 39--57. Nicholas Carlini and David Wagner. 2017b. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp). IEEE, 39--57."},{"key":"e_1_3_2_2_10_1","volume-title":"Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering. arxiv","author":"Chen Bryant","year":"1811","unstructured":"Bryant Chen , Wilka Carvalho , Nathalie Baracaldo , Heiko Ludwig , Benjamin Edwards , Taesung Lee , Ian Molloy , and Biplav Srivastava . 2018a. Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering. arxiv : 1811 .03728 [cs.LG] Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava. 2018a. Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering. arxiv: 1811.03728 [cs.LG]"},{"key":"e_1_3_2_2_11_1","volume-title":"Bita Darvish Rohani, and Farinaz Koushanfar","author":"Chen Huili","year":"2018","unstructured":"Huili Chen , Bita Darvish Rohani, and Farinaz Koushanfar . 2018 b. Deepmarks : A digital fingerprinting framework for deep neural networks. arXiv preprint arXiv:1804.03648 (2018). Huili Chen, Bita Darvish Rohani, and Farinaz Koushanfar. 2018b. Deepmarks: A digital fingerprinting framework for deep neural networks. arXiv preprint arXiv:1804.03648 (2018)."},{"key":"e_1_3_2_2_12_1","volume-title":"Refit: a unified watermark removal framework for deep learning systems with limited data. arXiv preprint arXiv:1911.07205","author":"Chen Xinyun","year":"2019","unstructured":"Xinyun Chen , Wenxiao Wang , Chris Bender , Yiming Ding , Ruoxi Jia , Bo Li , and Dawn Song . 2019. Refit: a unified watermark removal framework for deep learning systems with limited data. arXiv preprint arXiv:1911.07205 ( 2019 ). Xinyun Chen, Wenxiao Wang, Chris Bender, Yiming Ding, Ruoxi Jia, Bo Li, and Dawn Song. 2019. Refit: a unified watermark removal framework for deep learning systems with limited data. arXiv preprint arXiv:1911.07205 (2019)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3297858.3304051"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"e_1_3_2_2_15_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2018 . Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_2_16_1","volume-title":"Kam Woh Ng, and Chee Seng Chan","author":"Fan Lixin","year":"2019","unstructured":"Lixin Fan , Kam Woh Ng, and Chee Seng Chan . 2019 . Rethinking deep neural network ownership verification: Embedding passports to defeat ambiguity attacks. (2019). Lixin Fan, Kam Woh Ng, and Chee Seng Chan. 2019. Rethinking deep neural network ownership verification: Embedding passports to defeat ambiguity attacks. (2019)."},{"key":"e_1_3_2_2_17_1","volume-title":"Adversarial relighting against face recognition. arXiv preprint arXiv:2108.07920","author":"Gao Ruijun","year":"2021","unstructured":"Ruijun Gao , Qing Guo , Qian Zhang , Felix Juefei-Xu , Hongkai Yu , and Wei Feng . 2021. Adversarial relighting against face recognition. arXiv preprint arXiv:2108.07920 ( 2021 ). Ruijun Gao, Qing Guo, Qian Zhang, Felix Juefei-Xu, Hongkai Yu, and Wei Feng. 2021. Adversarial relighting against face recognition. arXiv preprint arXiv:2108.07920 (2021)."},{"key":"e_1_3_2_2_18_1","volume-title":"STRIP: A Defence Against Trojan Attacks on Deep Neural Networks. arxiv","author":"Gao Yansong","year":"2020","unstructured":"Yansong Gao , Chang Xu , Derui Wang , Shiping Chen , Damith C. Ranasinghe , and Surya Nepal . 2020 . STRIP: A Defence Against Trojan Attacks on Deep Neural Networks. arxiv : 1902.06531 [cs.CR] Yansong Gao, Chang Xu, Derui Wang, Shiping Chen, Damith C. Ranasinghe, and Surya Nepal. 2020. STRIP: A Defence Against Trojan Attacks on Deep Neural Networks. arxiv: 1902.06531 [cs.CR]"},{"key":"e_1_3_2_2_19_1","volume-title":"Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572","author":"Goodfellow Ian J","year":"2014","unstructured":"Ian J Goodfellow , Jonathon Shlens , and Christian Szegedy . 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 ( 2014 ). Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)."},{"key":"e_1_3_2_2_20_1","unstructured":"Tianyu Gu Brendan Dolan-Gavitt and Siddharth Garg. 2019. BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain. arxiv: 1708.06733 [cs.CR]  Tianyu Gu Brendan Dolan-Gavitt and Siddharth Garg. 2019. BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain. arxiv: 1708.06733 [cs.CR]"},{"key":"e_1_3_2_2_21_1","volume-title":"Fine-tuning is not enough: A simple yet effective watermark removal attack for dnn models. arXiv preprint arXiv:2009.08697","author":"Guo Shangwei","year":"2020","unstructured":"Shangwei Guo , Tianwei Zhang , Han Qiu , Yi Zeng , Tao Xiang , and Yang Liu . 2020. Fine-tuning is not enough: A simple yet effective watermark removal attack for dnn models. arXiv preprint arXiv:2009.08697 ( 2020 ). Shangwei Guo, Tianwei Zhang, Han Qiu, Yi Zeng, Tao Xiang, and Yang Liu. 2020. Fine-tuning is not enough: A simple yet effective watermark removal attack for dnn models. arXiv preprint arXiv:2009.08697 (2020)."},{"key":"e_1_3_2_2_22_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. https:\/\/doi.org\/10.48550\/ARXIV.1512.03385  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. https:\/\/doi.org\/10.48550\/ARXIV.1512.03385"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01448"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04434-z"},{"key":"e_1_3_2_2_25_1","volume-title":"Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks. arxiv","author":"Liu Kang","year":"1805","unstructured":"Kang Liu , Brendan Dolan-Gavitt , and Siddharth Garg . 2018. Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks. arxiv : 1805 .12185 [cs.CR] Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg. 2018. Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks. arxiv: 1805.12185 [cs.CR]"},{"key":"e_1_3_2_2_26_1","volume-title":"Removing Backdoor-Based Watermarks in Neural Networks with Limited Data. arXiv preprint arXiv:2008.00407","author":"Liu Xuankai","year":"2020","unstructured":"Xuankai Liu , Fengting Li , Bihan Wen , and Qi Li. 2020. Removing Backdoor-Based Watermarks in Neural Networks with Limited Data. arXiv preprint arXiv:2008.00407 ( 2020 ). Xuankai Liu, Fengting Li, Bihan Wen, and Qi Li. 2020. Removing Backdoor-Based Watermarks in Neural Networks with Limited Data. arXiv preprint arXiv:2008.00407 (2020)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_3_2_2_28_1","volume-title":"Sok: How robust is image classification deep neural network watermarking?(extended version). arXiv preprint arXiv:2108.04974","author":"Lukas Nils","year":"2021","unstructured":"Nils Lukas , Edward Jiang , Xinda Li , and Florian Kerschbaum . 2021 . Sok: How robust is image classification deep neural network watermarking?(extended version). arXiv preprint arXiv:2108.04974 (2021). Nils Lukas, Edward Jiang, Xinda Li, and Florian Kerschbaum. 2021. Sok: How robust is image classification deep neural network watermarking?(extended version). arXiv preprint arXiv:2108.04974 (2021)."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485133"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00265"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00363"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.771065"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3150226"},{"key":"e_1_3_2_2_34_1","volume-title":"Deepsigns: A generic watermarking framework for ip protection of deep learning models. arXiv preprint arXiv:1804.00750","author":"Rouhani Bita Darvish","year":"2018","unstructured":"Bita Darvish Rouhani , Huili Chen , and Farinaz Koushanfar . 2018 . Deepsigns: A generic watermarking framework for ip protection of deep learning models. arXiv preprint arXiv:1804.00750 (2018). Bita Darvish Rouhani, Huili Chen, and Farinaz Koushanfar. 2018. Deepsigns: A generic watermarking framework for ip protection of deep learning models. arXiv preprint arXiv:1804.00750 (2018)."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437880.3460401"},{"key":"e_1_3_2_2_36_1","volume-title":"On the robustness of the backdoor-based watermarking in deep neural networks. arXiv preprint arXiv:1906.07745","author":"Shafieinejad Masoumeh","year":"2019","unstructured":"Masoumeh Shafieinejad , Jiaqi Wang , Nils Lukas , Xinda Li , and Florian Kerschbaum . 2019. On the robustness of the backdoor-based watermarking in deep neural networks. arXiv preprint arXiv:1906.07745 ( 2019 ). Masoumeh Shafieinejad, Jiaqi Wang, Nils Lukas, Xinda Li, and Florian Kerschbaum. 2019. On the robustness of the backdoor-based watermarking in deep neural networks. arXiv preprint arXiv:1906.07745 (2019)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2021.3076778"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.908964"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3078971.3078974"},{"key":"e_1_3_2_2_40_1","volume-title":"Detect and remove watermark in deep neural networks via generative adversarial networks. arXiv preprint arXiv:2106.08104","author":"Wang Haoqi","year":"2021","unstructured":"Haoqi Wang , Mingfu Xue , Shichang Sun , Yushu Zhang , Jian Wang , and Weiqiang Liu . 2021. Detect and remove watermark in deep neural networks via generative adversarial networks. arXiv preprint arXiv:2106.08104 ( 2021 ). Haoqi Wang, Mingfu Xue, Shichang Sun, Yushu Zhang, Jian Wang, and Weiqiang Liu. 2021. Detect and remove watermark in deep neural networks via generative adversarial networks. arXiv preprint arXiv:2106.08104 (2021)."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00614"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3453688.3461752"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01457"},{"key":"e_1_3_2_2_44_1","volume-title":"Effectiveness of Distillation Attack and Countermeasure on Neural Network Watermarking. arxiv","author":"Yang Ziqi","year":"1906","unstructured":"Ziqi Yang , Hung Dang , and Ee-Chien Chang . 2019. Effectiveness of Distillation Attack and Countermeasure on Neural Network Watermarking. arxiv : 1906 .06046 [cs.CR] Ziqi Yang, Hung Dang, and Ee-Chien Chang. 2019. Effectiveness of Distillation Attack and Countermeasure on Neural Network Watermarking. arxiv: 1906.06046 [cs.CR]"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3064850"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3196494.3196550"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00729"}],"event":{"name":"MM '22: The 30th ACM International Conference on Multimedia","location":"Lisboa Portugal","acronym":"MM '22","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 30th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548390","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3548390","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T15:00:44Z","timestamp":1750172444000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548390"}},"subtitle":["Are Watermarks Robust against Naturalness-aware Perturbations?"],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":47,"alternative-id":["10.1145\/3503161.3548390","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3548390","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2022-10-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}