{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:18:55Z","timestamp":1773155935734,"version":"3.50.1"},"reference-count":79,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T00:00:00Z","timestamp":1744761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science and Technology Council","award":["112-2223-E-001-001, 111-2221-E-001-013-MY3, 111-2923-E-002-014-MY3, 112-2927-I-001-508, 113-2221-E-004-006-MY2, 113-2622-E-004-001, 113-2221-E-004-001-MY3, 112-2634-F-002-005, 113-2634-F-002-008, and 113-2923-E-A49-003-MY2"],"award-info":[{"award-number":["112-2223-E-001-001, 111-2221-E-001-013-MY3, 111-2923-E-002-014-MY3, 112-2927-I-001-508, 113-2221-E-004-006-MY2, 113-2622-E-004-001, 113-2221-E-004-001-MY3, 112-2634-F-002-005, 113-2634-F-002-008, and 113-2923-E-A49-003-MY2"]}]},{"name":"Academia Sinica","award":["AS-IA-111-M01"],"award-info":[{"award-number":["AS-IA-111-M01"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>Deep-fake videos, generated through AI face-swapping techniques, have garnered considerable attention due to their potential for impactful impersonation attacks. While existing research primarily distinguishes real from fake videos, attributing a deep-fake to its specific generation model or encoder is crucial for forensic investigation, enabling precise source tracing and tailored countermeasures. This approach not only enhances detection accuracy by leveraging unique model-specific artifacts but also provides insights essential for developing proactive defenses against evolving deep-fake techniques. Addressing this gap, this article investigates the model attribution problem for deep-fake videos using two datasets\u2014Deepfakes from Different Models (DFDM) and GANGen-Detection, which comprise deep-fake videos and images generated by GAN models. We select only fake images from the GANGen-Detection dataset to align with the DFDM dataset, which specifies the goal of this study, focusing on model attribution rather than real\/fake classification. This study formulates deep-fake model attribution as a multiclass classification task, introducing a novel Capsule-Spatial-Temporal (CapST) model that effectively integrates a modified VGG19 (utilizing only the first 26 out of 52 layers) for feature extraction, combined with Capsule Networks and a Spatio-Temporal attention mechanism. The Capsule module captures intricate feature hierarchies, enabling robust identification of deep-fake attributes, while a video-level fusion technique leverages temporal attention mechanisms to process concatenated feature vectors and capture temporal dependencies in deep-fake videos. By aggregating insights across frames, our model achieves a comprehensive understanding of video content, resulting in more precise predictions. Experimental results on the DFDM and GANGen-Detection datasets demonstrate the efficacy of CapST, achieving substantial improvements in accurately categorizing deep-fake videos over baseline models, all while demanding fewer computational resources.<\/jats:p>","DOI":"10.1145\/3715138","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T15:15:21Z","timestamp":1738163721000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["CapST: Leveraging Capsule Networks and Temporal Attention for Accurate Model Attribution in Deep-fake Videos"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4841-6481","authenticated-orcid":false,"given":"Wasim","family":"Ahmad","sequence":"first","affiliation":[{"name":"Social Networks and Human-Centered Computing, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan, and Department of Computer Science, National Chengchi University, Taipei, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3802-1670","authenticated-orcid":false,"given":"Yan-Tsung","family":"Peng","sequence":"additional","affiliation":[{"name":"Member IEEE, Department of Computer Science, National Chengchi University, Taipei, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1282-2111","authenticated-orcid":false,"given":"Yuan-Hao","family":"Chang","sequence":"additional","affiliation":[{"name":"Fellow IEEE, Institute of Information Science, Academia Sinica, Taipei, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8603-3062","authenticated-orcid":false,"given":"Gaddisa Olani","family":"Ganfure","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Dire Dawa University, Dire Dawa City, Ethiopia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0595-0448","authenticated-orcid":false,"given":"Sarwar","family":"Khan","sequence":"additional","affiliation":[{"name":"Social Networks and Human-Centered Computing, Taiwan International Graduate Program, Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan, and Department of Computer Science, National Chengchi University, Taipei, Taiwan Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,4,16]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/WIFS.2018.8630761"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00109"},{"issue":"9","key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"807","DOI":"10.32985\/ijeces.13.9.9","article-title":"ResViT: A framework for deepfake videos detection","volume":"13","author":"Ahmad Wasim","year":"2022","unstructured":"Wasim Ahmad, Imad Ali, Adil Shahzad, Ammarah Hashmi, and Faisal Ghaffar. 2022. ResViT: A framework for deepfake videos detection. International Journal of Electrical and Computer Engineering Systems 13, 9 (2022), 807\u2013813.","journal-title":"International Journal of Electrical and Computer Engineering Systems"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2018.00019"},{"key":"e_1_3_1_6_2","first-page":"1","volume-title":"Proceedings of the 2020 28th Signal Processing and Communications Applications Conference (SIU)","author":"Bekci Burak","year":"2020","unstructured":"Burak Bekci, Zahid Akhtar, and Haz\u0131m Kemal Ekenel. 2020. Cross-dataset face manipulation detection. In Proceedings of the 2020 28th Signal Processing and Communications Applications Conference (SIU). IEEE, 1\u20134."},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01380"},{"issue":"3","key":"e_1_3_1_8_2","doi-asserted-by":"crossref","first-page":"1468","DOI":"10.1109\/TCSVT.2022.3209336","article-title":"Learning features of intra-consistency and inter-diversity: Keys toward generalizable deepfake detection","volume":"33","author":"Chen Han","year":"2022","unstructured":"Han Chen, Yuzhen Lin, Bin Li, and Shunquan Tan. 2022. Learning features of intra-consistency and inter-diversity: Keys toward generalizable deepfake detection. IEEE Transactions on Circuits and Systems for Video Technology 33, 3 (2022), 1468\u20131480.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"e_1_3_1_9_2","first-page":"1","volume-title":"Proceedings of the 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","author":"Chen Yunzhuo","year":"2022","unstructured":"Yunzhuo Chen, Naveed Akhtar, Nur Al Hasan Haldar, and Ajmal Mian. 2022. Deepfake detection with spatio-temporal consistency and attention. In Proceedings of the 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 1\u20138."},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_3_1_11_2","first-page":"219","volume-title":"Proceedings of the 21st International Conference on Image Analysis and Processing (ICIAP \u201922), Part III","author":"Coccomini Davide Alessandro","year":"2022","unstructured":"Davide Alessandro Coccomini, Nicola Messina, Claudio Gennaro, and Fabrizio Falchi. 2022. Combining efficientnet and vision transformers for video deepfake detection. In Proceedings of the 21st International Conference on Image Analysis and Processing (ICIAP \u201922), Part III. Springer, 219\u2013229."},{"key":"e_1_3_1_12_2","first-page":"1","volume-title":"Proceedings of the 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG \u201921)","author":"Das Abhijit","year":"2021","unstructured":"Abhijit Das, Srijan Das, and Antitza Dantcheva. 2021. Demystifying attention mechanisms for deepfake detection. In Proceedings of the 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG \u201921). IEEE, 1\u20137."},{"key":"e_1_3_1_13_2","unstructured":"Oscar De Lima Sean Franklin Shreshtha Basu Blake Karwoski and Annet George. 2020. Deepfake detection using spatiotemporal convolutional networks. arXiv:2006.14749. Retrieved from https:\/\/arxiv.org\/abs\/2006.14749"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00300"},{"key":"e_1_3_1_15_2","unstructured":"B. Dolhansky J. Bitton B. Pflaum J. Lu R. Howes M. Wang and C. C. Ferrer. 2006. The deepfake detection challenge dataset. arXiv.2006.07397. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2006.07397"},{"key":"e_1_3_1_16_2","unstructured":"Brian Dolhansky Joanna Bitton Ben Pflaum Jikuo Lu Russ Howes Menglin Wang and Cristian Canton Ferrer. 2020. The deepfake detection challenge (DFDC) dataset. arXiv:2006.07397. Retrieved from https:\/\/arxiv.org\/abs\/2006.07397"},{"key":"e_1_3_1_17_2","unstructured":"Brian Dolhansky Russ Howes Ben Pflaum Nicole Baram and Cristian Canton Ferrer. 2019. The deepfake detection challenge (DFDC) preview dataset. arXiv:1910.08854. Retrieved from https:\/\/arxiv.org\/abs\/1910.08854"},{"key":"e_1_3_1_18_2","unstructured":"Ricard Durall Margret Keuper Franz-Josef Pfreundt and Janis Keuper. 2019. Unmasking deepfakes with simple features. arXiv:1911.00686. Retrieved from https:\/\/arxiv.org\/abs\/1911.00686"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3050065"},{"key":"e_1_3_1_20_2","unstructured":"Ipek Ganiyusufoglu L. Minh Ng\u00f4 Nedko Savov Sezer Karaoglu and Theo Gevers. 2020. Spatio-temporal features for generalized detection of deepfake videos. arXiv:2010.11844. Retrieved from https:\/\/arxiv.org\/abs\/2010.11844"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01383"},{"key":"e_1_3_1_22_2","unstructured":"Deepfakes Github. n. d. Retrieved January 6 2024 from https:\/\/github.com\/deepfakes\/faceswap"},{"key":"e_1_3_1_23_2","unstructured":"DeepFaceLab Github. n. d. Retrieved January 6 2024 from https:\/\/github.com\/iperov\/DeepFaceLab"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475508"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00341"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.23919\/APSIPAASC55919.2022.9980255"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3074259"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_3_1_30_2","first-page":"2356","volume-title":"Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP)","author":"Jia Shan","year":"2022","unstructured":"Shan Jia, Xin Li, and Siwei Lyu. 2022. Model attribution of face-swap deepfake videos. In Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2356\u20132360."},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00296"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"e_1_3_1_33_2","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/978-3-031-78305-0_11","volume-title":"Proceedings of the International Conference on Pattern Recognition","author":"Keita Mamadou","year":"2025","unstructured":"Mamadou Keita, Wassim Hamidouche, Bougueffa Eutamene Hessen, Abdelmalik Taleb-Ahmed, and Hadid Abdenour. 2025. FIDAVL: Fake image detection and attribution using vision-language model. In Proceedings of the International Conference on Pattern Recognition. Springer, 160\u2013176."},{"key":"e_1_3_1_34_2","first-page":"1001","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Kim Minha","year":"2021","unstructured":"Minha Kim, Shahroz Tariq, and Simon S. Woo. 2021. FRETAL: Generalizing deepfake detection using knowledge distillation and representation learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 1001\u20131012."},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00639"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"e_1_3_1_37_2","first-page":"399","volume-title":"Proceedings of the 16th European Conference on Computer Vision (ECCV \u201920), Part IX","author":"Li Xiaoming","year":"2020","unstructured":"Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo, and Lei Zhang. 2020. Blind face restoration via deep multi-scale component dictionaries. In Proceedings of the 16th European Conference on Computer Vision (ECCV \u201920), Part IX. Springer, 399\u2013415."},{"issue":"4","key":"e_1_3_1_38_2","first-page":"1658","article-title":"Artifacts-disentangled adversarial learning for deepfake detection","volume":"33","author":"Li Xin","year":"2022","unstructured":"Xin Li, Rongrong Ni, Pengpeng Yang, Zhiqiang Fu, and Yao Zhao. 2022. Artifacts-disentangled adversarial learning for deepfake detection. IEEE Transactions on Circuits and Systems for Video Technology 33, 4 (2022), 1658\u20131670.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"e_1_3_1_39_2","first-page":"46","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","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 Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 46-52."},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICMEW46912.2020.9105991"},{"key":"e_1_3_1_43_2","first-page":"506","volume-title":"Proceedings of the 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","author":"Marra Francesco","year":"2019","unstructured":"Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, and Giovanni Poggi. 2019. Do GANs leave artificial fingerprints? In Proceedings of the 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 506\u2013511."},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8803603"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413570"},{"key":"e_1_3_1_46_2","doi-asserted-by":"crossref","unstructured":"Arsha Nagrani Joon Son Chung and Andrew Zisserman. 2017. VoxCeleb: A large-scale speaker identification dataset. arXiv:1706.08612. Retrieved from https:\/\/arxiv.org\/abs\/1706.08612","DOI":"10.21437\/Interspeech.2017-950"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3230833.3230863"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682602"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02345"},{"key":"e_1_3_1_50_2","first-page":"319","volume-title":"Proceedings of the 16th European Conference on Computer Vision (ECCV \u201920), Part IX","author":"Park Taesung","year":"2020","unstructured":"Taesung Park, Alexei A. Efros, Richard Zhang, and Jun-Yan Zhu. 2020. Contrastive learning for unpaired image-to-image translation. In Proceedings of the 16th European Conference on Computer Vision (ECCV \u201920), Part IX. Springer, 319\u2013345."},{"key":"e_1_3_1_51_2","first-page":"1","volume-title":"Proceedings of the 2017 IEEE Workshop on Information Forensics and Security (WIFS)","author":"Rahmouni Nicolas","year":"2017","unstructured":"Nicolas Rahmouni, Vincent Nozick, Junichi Yamagishi, and Isao Echizen. 2017. Distinguishing computer graphics from natural images using convolution neural networks. In Proceedings of the 2017 IEEE Workshop on Information Forensics and Security (WIFS). IEEE, 1\u20136."},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00009"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_1_54_2","first-page":"3856","volume-title":"In Proceedings of the 31st Advances in Neural Information Processing Systems (NeuralPS\u201917)","author":"Sabour Sara","year":"2017","unstructured":"Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton. 2017. Dynamic routing between capsules. In Proceedings of the 31st Advances in Neural Information Processing Systems (NeuralPS\u201917), 3856\u20133866."},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_1_56_2","unstructured":"Selim Seferbekov. 2020. DFDC 1st Place Solution. Retrieved from https:\/\/www.kaggle.com\/c\/deepfake-detection-challenge."},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3576915.3616588"},{"key":"e_1_3_1_58_2","unstructured":"shanface33. 2023. Deepfake Model Attribution Issue 2. Retrieved May 11 2023 from https:\/\/github.com\/shanface33\/Deepfake_Model_Attribution\/issues\/2"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00227"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_1_61_2","unstructured":"Chuang Chuang Tan Jiahui Li and Feng Wu. 2024. AIGC Dataset: A Dataset for Detection of AI-Generated Content (GANGen). GitHub Repository. Retrieved from https:\/\/github.com\/chuangchuangtan\/GANGen-Detection"},{"key":"e_1_3_1_62_2","first-page":"6105","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc Le. 2019. EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the International Conference on Machine Learning. PMLR, 6105\u20136114."},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/2929464.2929475"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00565"},{"key":"e_1_3_1_65_2","first-page":"374","volume-title":"Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","author":"Wang Zhibing","year":"2021","unstructured":"Zhibing Wang, Xin Li, Rongrong Ni, and Yao Zhao. 2021. Attention guided spatio-temporal artifacts extraction for deepfake detection. In Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Springer, 374\u2013386."},{"key":"e_1_3_1_66_2","unstructured":"Deressa Wodajo and Solomon Atnafu. 2021. 2021. Deepfake video detection using convolutional vision transformer. arXiv:2102.11126. Retrieved from https:\/\/arxiv.org\/abs\/2102.11126"},{"key":"e_1_3_1_67_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2018.2833032"},{"key":"e_1_3_1_69_2","first-page":"3083","volume-title":"Proceedings of the 35th AAAI Conference on Artificial Intelligence","author":"Xu Zhiliang","year":"2021","unstructured":"Zhiliang Xu, Xiyu Yu, Zhibin Hong, Zhen Zhu, Junyu Han, Jingtuo Liu, Errui Ding, and Xiang Bai. 2021. FaceController: Controllable attribute editing for face in the wild. In Proceedings of the 35th AAAI Conference on Artificial Intelligence. AAAI, 3083\u20133091."},{"key":"e_1_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3133859"},{"key":"e_1_3_1_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3290752"},{"key":"e_1_3_1_72_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00765"},{"key":"e_1_3_1_73_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3146781"},{"key":"e_1_3_1_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3281448"},{"issue":"2","key":"e_1_3_1_75_2","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/TCSVT.2022.3207310","article-title":"A local perturbation generation method for GAN-generated face anti-forensics","volume":"33","author":"Zhang Haitao","year":"2022","unstructured":"Haitao Zhang, Beijing Chen, Jinwei Wang, and Guoying Zhao. 2022. A local perturbation generation method for GAN-generated face anti-forensics. IEEE Transactions on Circuits and Systems for Video Technology 33, 2 (2022), 661\u2013676.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"e_1_3_1_76_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"e_1_3_1_77_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01477"},{"key":"e_1_3_1_78_2","doi-asserted-by":"crossref","first-page":"1831","DOI":"10.1109\/CVPRW.2017.229","volume-title":"Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","author":"Zhou Peng","year":"2017","unstructured":"Peng Zhou, Xintong Han, Vlad I. Morariu, and Larry S. Davis. 2017. Two-stream neural networks for tampered face detection. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 1831\u20131839."},{"key":"e_1_3_1_79_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00480"},{"key":"e_1_3_1_80_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413769"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3715138","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3715138","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:18Z","timestamp":1750295898000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3715138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,16]]},"references-count":79,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4,30]]}},"alternative-id":["10.1145\/3715138"],"URL":"https:\/\/doi.org\/10.1145\/3715138","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,16]]},"assertion":[{"value":"2024-07-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-28","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}