{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T14:52:42Z","timestamp":1780930362249,"version":"3.54.1"},"reference-count":49,"publisher":"Association for Computing Machinery (ACM)","issue":"11","license":[{"start":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:00Z","timestamp":1726099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science Foundation","award":["2129173 and 2235135"],"award-info":[{"award-number":["2129173 and 2235135"]}]},{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"crossref","award":["13106715"],"award-info":[{"award-number":["13106715"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>With the advances in generative adversarial networks (GAN), facial manipulations called DeepFakes have caused major security risks and raised severe societal concerns. However, the popular DeepFake passive detection is an ex-post forensics countermeasure and fails in blocking the disinformation spread in advance. Alternatively, precautions such as adding perturbations to the real data for unnatural distorted DeepFake output easily spotted by the human eyes are introduced as proactive defenses. Recent studies suggest that these existing proactive defenses can be easily bypassed by employing simple image transformation and reconstruction techniques when applied to the perturbed real data and the distorted output, respectively. The aim of this article is to propose a novel proactive DeepFake detection technique using GAN-based visible watermarking. To this front, we propose a reconstructive regularization added to the GAN\u2019s loss function that embeds a unique watermark to the assigned location of the generated fake image. Thorough experiments on multiple datasets confirm the viability of the proposed approach as a proactive defense mechanism against DeepFakes from the perspective of detection by human eyes. Thus, our proposed watermark-based GANs prevent the abuse of the pretrained GANs and smartphone apps, available via online repositories, for DeepFake creation for malicious purposes. Further, the watermarked DeepFakes can also be detected by the SOTA DeepFake detectors. This is critical for applications where automatic DeepFake detectors are used for mass audits due to the huge cost associated with human observers examining a large amount of data manually.<\/jats:p>","DOI":"10.1145\/3625547","type":"journal-article","created":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T08:09:03Z","timestamp":1695542943000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":54,"title":["ProActive DeepFake Detection using GAN-based Visible Watermarking"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7254-3207","authenticated-orcid":false,"given":"Aakash Varma","family":"Nadimpalli","sequence":"first","affiliation":[{"name":"Wichita State University, Wichita, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1541-8202","authenticated-orcid":false,"given":"Ajita","family":"Rattani","sequence":"additional","affiliation":[{"name":"University of North Texas, Denton, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,9,12]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/WIFS.2018.8630761"},{"key":"e_1_3_3_3_1","volume-title":"Proceedings of the CVPR Workshops","author":"Agarwal Shruti","year":"2019","unstructured":"Shruti Agarwal, Hany Farid, Yuming Gu, Mingming He, Koki Nagano, and Hao Li. 2019. Protecting world leaders against deep fakes. In Proceedings of the CVPR Workshops."},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/info11020110"},{"key":"e_1_3_3_5_1","doi-asserted-by":"crossref","unstructured":"C. Chan S. Ginosar T. Zhou and A. Efros. 2019. Everybody dance now. 2019 IEEE\/CVF International Conference on Computer Vision (ICCV) Seoul 5932\u20135941.","DOI":"10.1109\/ICCV.2019.00603"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.04.014"},{"key":"e_1_3_3_7_1","doi-asserted-by":"crossref","unstructured":"Huili Chen Bita Darvish Rouhani and Farinaz Koushanfar. 2018. DeepMarks: A digital fingerprinting framework for deep neural networks. IACR Cryptol. ePrint Arch. (2018) 322.","DOI":"10.1145\/3323873.3325042"},{"key":"e_1_3_3_8_1","first-page":"9010","article-title":"MagDR: Mask-guided detection and reconstruction for defending deepfakes","author":"Chen Zhikai","year":"2021","unstructured":"Zhikai Chen, Lingxi Xie, Shanmin Pang, Yong He, and Bo Zhang. 2021. MagDR: Mask-guided detection and reconstruction for defending deepfakes. 2021 IEEE CVPR (2021), 9010\u20139019.","journal-title":"2021 IEEE CVPR"},{"key":"e_1_3_3_9_1","first-page":"8789","article-title":"StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation","author":"Choi Yunjey","year":"2017","unstructured":"Yunjey Choi, Min-Je Choi, Mun Su. Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. 2017. StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation. 2018 IEEE CVPR (2017), 8789\u20138797.","journal-title":"2018 IEEE CVPR"},{"key":"e_1_3_3_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_3_3_11_1","unstructured":"Xiaoyi Dong Jianmin Bao Dongdong Chen Weiming Zhang Nenghai Yu Dong Chen Fang Wen and Baining Guo. 2020. Identity-driven deepfake detection. ArXiv abs\/2012.03930."},{"key":"e_1_3_3_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00500"},{"key":"e_1_3_3_13_1","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He Kaiming","year":"2016","unstructured":"Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (2016), 770\u2013778.","journal-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_3_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2916751"},{"key":"e_1_3_3_15_1","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Heusel Martin","year":"2017","unstructured":"Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_3_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00296"},{"key":"e_1_3_3_17_1","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980."},{"key":"e_1_3_3_18_1","doi-asserted-by":"publisher","DOI":"10.1080\/02699930903485076"},{"key":"e_1_3_3_19_1","unstructured":"Binh Le Shahroz Tariq Alsharif Abuadbba Kristen Moore and Simon Woo. 2023. Why do facial deepfake detectors fail? In Proceedings of the 2nd Workshop on Security Implications of Deepfakes and Cheapfakes WDC ACM 2023 New York NY 24\u201328."},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"e_1_3_3_21_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops","author":"Li Yuezun","year":"2019","unstructured":"Yuezun Li and Siwei Lyu. 2019. Exposing deepfake videos by detecting face warping artifacts. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops."},{"key":"e_1_3_3_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"e_1_3_3_23_1","first-page":"3684","article-title":"WDNet: Watermark-decomposition network for visible watermark removal","author":"Liu Yang","year":"2020","unstructured":"Yang Liu, Zhen Zhu, and Xiang Bai. 2020. WDNet: Watermark-decomposition network for visible watermark removal. 2021 IEEE WACV (2020), 3684\u20133692.","journal-title":"2021 IEEE WACV"},{"key":"e_1_3_3_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_3_3_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58571-6_39"},{"key":"e_1_3_3_26_1","doi-asserted-by":"crossref","unstructured":"A. V. Nadimpalli and A. Rattani. 2023. Gbdf: Gender balanced deepfake dataset towards fair deepfake detection. In Pattern Recognition Computer Vision and Image Processing. ICPR 2022 International Workshops and Challenges: Montreal QC Canada August 21\u201325 2022 Proceedings Part II Springer-Verlag Berlin Heidelberg 320\u2013337.","DOI":"10.1007\/978-3-031-37742-6_25"},{"key":"e_1_3_3_27_1","first-page":"91","article-title":"On improving cross-dataset generalization of deepfake detectors","author":"Nadimpalli Aakash Varma","year":"2022","unstructured":"Aakash Varma Nadimpalli and Ajita Rattani. 2022a. On improving cross-dataset generalization of deepfake detectors. 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (2022), 91\u201399.","journal-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops"},{"key":"e_1_3_3_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2022.103525"},{"key":"e_1_3_3_29_1","doi-asserted-by":"crossref","unstructured":"Y. Nirkin Y. Keller and T. Hassner. 2023. Fsganv2: Improved subject agnostic face swapping and reenactment. IEEE Transactions on Pattern Analysis and Machine Intelligence 45 1 (2023) 560\u2013575.","DOI":"10.1109\/TPAMI.2022.3155571"},{"key":"e_1_3_3_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00363"},{"key":"e_1_3_3_31_1","first-page":"1","article-title":"DFGC 2022: The second deepfake game competition","author":"Peng Bo","year":"2022","unstructured":"Bo Peng, Wei Xiang, Yue Jiang, Wei Wang, Jing Dong, Zhen Sun, Zhen Lei, and Siwei Lyu. 2022. DFGC 2022: The second deepfake game competition. 2022 IEEE International Joint Conference on Biometrics (2022), 1\u201310.","journal-title":"2022 IEEE International Joint Conference on Biometrics"},{"key":"e_1_3_3_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"e_1_3_3_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCST49569.2021.9717407"},{"key":"e_1_3_3_34_1","doi-asserted-by":"crossref","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.","DOI":"10.1145\/3297858.3304051"},{"key":"e_1_3_3_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-66823-5_14"},{"key":"e_1_3_3_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00009"},{"key":"e_1_3_3_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_3_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"e_1_3_3_39_1","first-page":"10096","volume-title":"Proceedings of the International conference on machine learning","author":"Tan Mingxing","year":"2021","unstructured":"Mingxing Tan and Quoc Le. 2021. Efficientnetv2: Smaller models and faster training. In Proceedings of the International conference on machine learning. PMLR, 10096\u201310106."},{"key":"e_1_3_3_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.06.014"},{"key":"e_1_3_3_41_1","doi-asserted-by":"crossref","unstructured":"Loc Trinh and Y. Liu. 2021. An examination of fairness of AI models for deepfake detection. In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI\u201921) Z.-H. Zhou (Ed.). International Joint Conferences on Artificial Intelligence Organization 567\u2013574.","DOI":"10.24963\/ijcai.2021\/79"},{"key":"e_1_3_3_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3078971.3078974"},{"key":"e_1_3_3_43_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/107"},{"key":"e_1_3_3_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01450"},{"key":"e_1_3_3_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"e_1_3_3_46_1","doi-asserted-by":"publisher","DOI":"10.22215\/timreview\/1282"},{"key":"e_1_3_3_47_1","first-page":"14428","article-title":"Artificial fingerprinting for generative models: Rooting deepfake attribution in training data","author":"Yu Ning","year":"2020","unstructured":"Ning Yu, Vladislav Skripniuk, Sahar Abdelnabi, and Mario Fritz. 2020. Artificial fingerprinting for generative models: Rooting deepfake attribution in training data. 2021 IEEE ICCV (2020), 14428\u201314437.","journal-title":"2021 IEEE ICCV"},{"key":"e_1_3_3_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2016.2603342"},{"key":"e_1_3_3_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"e_1_3_3_50_1","unstructured":"Xin Zhong Arjon Das Fahad Alrasheedi and Abdullah Tanvir. 2023. Deep learning based image watermarking: A brief survey. arXiv:2308.04603. Retrieved from https:\/\/arxiv.org\/abs\/2308.04603"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3625547","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3625547","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:50:37Z","timestamp":1750287037000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3625547"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,12]]},"references-count":49,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11,30]]}},"alternative-id":["10.1145\/3625547"],"URL":"https:\/\/doi.org\/10.1145\/3625547","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,12]]},"assertion":[{"value":"2023-04-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}