{"type":"ScholarlyArticle","title":"Pluggable Watermarking of Deepfake Models for Deepfake Detection","authors":[{"name":"Han Bao","slug":"han-bao","given":"Han","family":"Bao","affiliation":"School of Software Technology , Zhejiang University."},{"name":"Xuhong Zhang","slug":"xuhong-zhang","given":"Xuhong","family":"Zhang","affiliation":"School of Software Technology , Zhejiang University."},{"name":"Qinying Wang","slug":"qinying-wang","given":"Qinying","family":"Wang","affiliation":"College of Computer Science , Zhejiang University"},{"name":"Kangming Liang","slug":"kangming-liang","given":"Kangming","family":"Liang","affiliation":"College of Engineering , Zhejiang University."},{"name":"Zonghui Wang","slug":"zonghui-wang","given":"Zonghui","family":"Wang","affiliation":"College of Computer Science , Zhejiang University"},{"name":"Shouling Ji","slug":"shouling-ji","given":"Shouling","family":"Ji","affiliation":"College of Computer Science , Zhejiang University"},{"name":"Wenzhi Chen","slug":"wenzhi-chen","given":"Wenzhi","family":"Chen","affiliation":"College of Computer Science , Zhejiang University"}],"abstract":"Deepfake model misuse poses major security concerns. Existing passive and active Deepfake detection methods both suffer from a lack of generalizability and robustness. In this study, we propose a pluggable and efficient active model watermarking framework for Deepfake detection. \r\nThis approach facilitates the embedding of identification watermarks across a variety of Deepfake generation models, enabling easy extraction by authorities for detection purposes. \r\nSpecifically, our method leverages the universal convolutional structure in generative model decoders. It employs convolutional kernel sparsification for adaptive watermark embedding positioning and introduces convolutional kernel normalization to seamlessly integrate watermark parameters with those of the generative model.\r\nFor watermark extraction, we jointly train a watermark extractor based on a Deepfake detection model and use BCH encoding to identify watermark images effectively.\r\nFinally, we apply our approach to eight major types of Deepfake generation models.\r\nExperiments show our method successfully detects Deepfakes with an average accuracy exceeding 94% even in heavy lossy channels.\r\nThis approach operates independently of the generation model's training without affecting the original model's performance.\r\nFurthermore, our model requires training a very limited number of parameters, and it is resilient against three major adaptive attacks.\r\nThe source code can be found at https://github.com/GuaiZao/Pluggable-Watermarking","doi":"10.24963/ijcai.2024/37","url":"https://ijcai.org/proceedings/2024/37","pdf":"https://ijcai.org/proceedings/2024/0037.pdf","markdown":"https://ijcai.org/proceedings/2024/37.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/37","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI Ethics, Trust, Fairness","track":"Main Track","number":37,"pages":{"first":331,"last":339},"keywords":["AI Ethics, Trust, Fairness: ETF: Trustworthy AI","AI Ethics, Trust, Fairness: ETF: Safety and robustness"],"videos":[],"references":[{"position":1,"raw":"[Cao et al., 2022] Hanqun Cao, Cheng Tan, Zhangyang Gao, Guangyong Chen, Pheng-Ann Heng, and Stan Z Li. 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