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Unlike passive detection approaches that operate after DFs have been created and distributed, proactive defense mechanisms aim at preventing the generation of malicious synthetic content at its source. This article provides a comprehensive survey of current proactive DF defense strategies, including Disruption and Watermarking. Disruption approaches protect individuals\u2019 data by introducing imperceptible perturbations that prevent unauthorized exploitation by generative models, while watermarking approaches embed verifiable messages into data or models to enable content authentication and attribution. We also analyze proactive approaches across various evaluation metrics (imperceptibility, protectability\/detectability, transferability, traceability, and robustness), and examine their effectiveness in real-world settings. Furthermore, we review the evolution of DF generation techniques, highlighting their rapid developments. Finally, we identify key challenges and promising future research directions to enhance proactive defense mechanisms.<\/jats:p>","DOI":"10.1145\/3771296","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T11:38:33Z","timestamp":1759923513000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["A Survey on Proactive Deepfake Defense: Disruption and Watermarking"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1553-2264","authenticated-orcid":false,"given":"Hong-Hanh","family":"Nguyen-Le","sequence":"first","affiliation":[{"name":"School of Computer Science, University College Dublin","place":["Dublin, Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5859-3855","authenticated-orcid":false,"given":"Van-Tuan","family":"Tran","sequence":"additional","affiliation":[{"name":"School of Computer Science and Statistics, Trinity College Dublin","place":["Dublin, Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0524-8841","authenticated-orcid":false,"given":"Thuc","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Vietnam National University Ho Chi Minh City University of Science","place":["Ho Chi Minh City, Viet Nam"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4373-2212","authenticated-orcid":false,"given":"Nhien-An","family":"Le-Khac","sequence":"additional","affiliation":[{"name":"School of Computer Science, University College Dublin","place":["Dublin, Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"2020. 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Retrieved July 29 2024 from https:\/\/arstechnica.com\/tech-policy\/2023\/03\/fake-ai-generated-images-imagining-donald-trumps-arrest-circulate-on-twitter"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01495"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01188"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3669754.3669799"},{"key":"e_1_3_2_12_2","unstructured":"BAIO. 2023. Invasive Diffusion: How one unwilling illustrator found herself turned into an AI model. Retrieved March 07 2025 from https:\/\/www.reddit.com\/r\/StableDiffusion\/comments\/yjktxu\/article_about_a_model_released_on_this_subreddit\/"},{"key":"e_1_3_2_13_2","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","author":"Bao Han","year":"2024","unstructured":"Han Bao, Xuhong Zhang, Qinying Wang, Kangming Liang, Zonghui Wang, Shouling Ji, and Wenzhi Chen. 2024. 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[2021]. Unlearnable examples: Making personal data unexploitable. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i1.19982"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3285981"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i2.16254"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00589"},{"key":"e_1_3_2_65_2","unstructured":"IndustryTrends. 2022. Expert feels Lensa AI is pilfering From Human Art. 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In Proceedings of the SLTU. 63\u201368."},{"key":"e_1_3_2_70_2","first-page":"17022","article-title":"Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis","volume":"33","author":"Kong Jungil","year":"2020","unstructured":"Jungil Kong, Jaehyeon Kim, and Jaekyoung Bae. 2020. Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis. Advances in Neural Information Processing Systems 33 (2020), 17022\u201317033.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/QoMEX.2012.6263880"},{"key":"e_1_3_2_72_2","first-page":"1428","volume-title":"Proceedings of the 27th ACM International Conference on Multimedia","author":"KR Prajwal","year":"2019","unstructured":"Prajwal KR, Rudrabha Mukhopadhyay, Jerin Philip, Abhishek Jha, Vinay Namboodiri, and CV Jawahar. 2019. Towards automatic face-to-face translation. 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