{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T22:05:04Z","timestamp":1769724304953,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":54,"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":[{"name":"the National Key Research and Development Program of China","award":["No.2020AAA0140003"],"award-info":[{"award-number":["No.2020AAA0140003"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972395"],"award-info":[{"award-number":["61972395"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3503161.3547923","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T15:42:46Z","timestamp":1665416566000},"page":"2464-2472","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Defeating DeepFakes via Adversarial Visual Reconstruction"],"prefix":"10.1145","author":[{"given":"Ziwen","family":"He","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Weinan","family":"Guan","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Jing","family":"Dong","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Tieniu","family":"Tan","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Rameen Abdal Yipeng Qin and Peter Wonka. 2019. Image2stylegan: How to embed images into the stylegan latent space?. In ICCV. 4432--4441. Rameen Abdal Yipeng Qin and Peter Wonka. 2019. Image2stylegan: How to embed images into the stylegan latent space?. In ICCV. 4432--4441.","DOI":"10.1109\/ICCV.2019.00453"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Darius Afchar Vincent Nozick Junichi Yamagishi and Isao Echizen. 2018. Mesonet: a compact facial video forgery detection network. In WIFS. 1--7. Darius Afchar Vincent Nozick Junichi Yamagishi and Isao Echizen. 2018. Mesonet: a compact facial video forgery detection network. In WIFS. 1--7.","DOI":"10.1109\/WIFS.2018.8630761"},{"key":"e_1_3_2_2_3_1","first-page":"2106","article-title":"Image transformation based defense against adversarial perturbation on deep learning models","volume":"18","author":"Agarwal Akshay","year":"2020","unstructured":"Akshay Agarwal , Richa Singh , Mayank Vatsa , and Nalini K Ratha . 2020 . Image transformation based defense against adversarial perturbation on deep learning models . IEEE Transactions on Dependable and Secure Computing , Vol. 18 , 5 (2020), 2106 -- 2121 . Akshay Agarwal, Richa Singh, Mayank Vatsa, and Nalini K Ratha. 2020. Image transformation based defense against adversarial perturbation on deep learning models. IEEE Transactions on Dependable and Secure Computing, Vol. 18, 5 (2020), 2106--2121.","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"e_1_3_2_2_4_1","unstructured":"Shruti Agarwal Hany Farid Yuming Gu Mingming He Koki Nagano and Hao Li. 2019. Protecting World Leaders Against Deep Fakes.. In CVPRW. 38--45. Shruti Agarwal Hany Farid Yuming Gu Mingming He Koki Nagano and Hao Li. 2019. Protecting World Leaders Against Deep Fakes.. In CVPRW. 38--45."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Renwang Chen Xuanhong Chen Bingbing Ni and Yanhao Ge. 2020. SimSwap: An Efficient Framework For High Fidelity Face Swapping. In ACMMM. 2003--2011. Renwang Chen Xuanhong Chen Bingbing Ni and Yanhao Ge. 2020. SimSwap: An Efficient Framework For High Fidelity Face Swapping. In ACMMM. 2003--2011.","DOI":"10.1145\/3394171.3413630"},{"key":"e_1_3_2_2_6_1","volume-title":"Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In CVPR. 8789--8797.","author":"Choi Yunjey","year":"2018","unstructured":"Yunjey Choi , Minje Choi , Munyoung Kim , Jung-Woo Ha , Sunghun Kim , and Jaegul Choo . 2018 . Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In CVPR. 8789--8797. Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. 2018. Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In CVPR. 8789--8797."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Jesse Davis and Mark Goadrich. 2006. The relationship between Precision-Recall and ROC curves. In ICML. 233--240. Jesse Davis and Mark Goadrich. 2006. The relationship between Precision-Recall and ROC curves. In ICML. 233--240.","DOI":"10.1145\/1143844.1143874"},{"key":"e_1_3_2_2_8_1","volume-title":"Arcface: Additive angular margin loss for deep face recognition. In CVPR. 4690--4699.","author":"Deng Jiankang","year":"2019","unstructured":"Jiankang Deng , Jia Guo , Niannan Xue , and Stefanos Zafeiriou . 2019 . Arcface: Additive angular margin loss for deep face recognition. In CVPR. 4690--4699. Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. 2019. Arcface: Additive angular margin loss for deep face recognition. In CVPR. 4690--4699."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Apurva Gandhi and Shomik Jain. 2020. Adversarial Perturbations Fool Deepfake Detectors. In IJCNN. 1--8. Apurva Gandhi and Shomik Jain. 2020. Adversarial Perturbations Fool Deepfake Detectors. In IJCNN. 1--8.","DOI":"10.1109\/IJCNN48605.2020.9207034"},{"key":"e_1_3_2_2_10_1","unstructured":"Ian J. Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative Adversarial Nets. In NIPS. 2672--2680. Ian J. Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative Adversarial Nets. In NIPS. 2672--2680."},{"key":"e_1_3_2_2_11_1","volume-title":"Robust Face-Swap Detection Based on 3D Facial Shape Information. arXiv preprint arXiv:2104.13665","author":"Guan Weinan","year":"2021","unstructured":"Weinan Guan , Wei Wang , Jing Dong , Bo Peng , and Tieniu Tan . 2021. Robust Face-Swap Detection Based on 3D Facial Shape Information. arXiv preprint arXiv:2104.13665 ( 2021 ). Weinan Guan, Wei Wang, Jing Dong, Bo Peng, and Tieniu Tan. 2021. Robust Face-Swap Detection Based on 3D Facial Shape Information. arXiv preprint arXiv:2104.13665 (2021)."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Keke He Zhanxiong Wang Yanwei Fu Rui Feng Yu-Gang Jiang and Xiangyang Xue. 2017. Adaptively weighted multi-task deep network for person attribute classification. In ACMMM. 1636--1644. Keke He Zhanxiong Wang Yanwei Fu Rui Feng Yu-Gang Jiang and Xiangyang Xue. 2017. Adaptively weighted multi-task deep network for person attribute classification. In ACMMM. 1636--1644.","DOI":"10.1145\/3123266.3123424"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2916751"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Qidong Huang Jie Zhang Wenbo Zhou Weiming Zhang and Nenghai Yu. 2021. Initiative Defense against Facial Manipulation. In AAAI. 1619--1627. Qidong Huang Jie Zhang Wenbo Zhou Weiming Zhang and Nenghai Yu. 2021. Initiative Defense against Facial Manipulation. In AAAI. 1619--1627.","DOI":"10.1609\/aaai.v35i2.16254"},{"key":"e_1_3_2_2_15_1","volume-title":"McAuley","author":"Hussain Shehzeen","year":"2021","unstructured":"Shehzeen Hussain , Paarth Neekhara , Malhar Jere , Farinaz Koushanfar , and Julian J . McAuley . 2021 . Adversarial Deepfakes : Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples. In WACV. 3347--3356. Shehzeen Hussain, Paarth Neekhara, Malhar Jere, Farinaz Koushanfar, and Julian J. McAuley. 2021. Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples. In WACV. 3347--3356."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Justin Johnson Alexandre Alahi and Li Fei-Fei. 2016. Perceptual losses for real-time style transfer and super-resolution. In ECCV. 694--711. Justin Johnson Alexandre Alahi and Li Fei-Fei. 2016. Perceptual losses for real-time style transfer and super-resolution. In ECCV. 694--711.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"Amin Jourabloo and Xiaoming Liu. 2015. Pose-invariant 3D face alignment. In ICCV. 3694--3702. Amin Jourabloo and Xiaoming Liu. 2015. Pose-invariant 3D face alignment. In ICCV. 3694--3702.","DOI":"10.1109\/ICCV.2015.421"},{"key":"e_1_3_2_2_18_1","unstructured":"Tero Karras Timo Aila Samuli Laine and Jaakko Lehtinen. 2018. Progressive Growing of GANs for Improved Quality Stability and Variation. In ICLR. Tero Karras Timo Aila Samuli Laine and Jaakko Lehtinen. 2018. Progressive Growing of GANs for Improved Quality Stability and Variation. In ICLR."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"crossref","unstructured":"Tero Karras Samuli Laine and Timo Aila. 2019. A style-based generator architecture for generative adversarial networks. In CVPR. 4401--4410. Tero Karras Samuli Laine and Timo Aila. 2019. A style-based generator architecture for generative adversarial networks. In CVPR. 4401--4410.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Tero Karras Samuli Laine Miika Aittala Janne Hellsten Jaakko Lehtinen and Timo Aila. 2020. Analyzing and improving the image quality of stylegan. In CVPR. 8110--8119. Tero Karras Samuli Laine Miika Aittala Janne Hellsten Jaakko Lehtinen and Timo Aila. 2020. Analyzing and improving the image quality of stylegan. In CVPR. 8110--8119.","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Iryna Korshunova Wenzhe Shi Joni Dambre and Lucas Theis. 2017. Fast face-swap using convolutional neural networks. In ICCV. 3677--3685. Iryna Korshunova Wenzhe Shi Joni Dambre and Lucas Theis. 2017. Fast face-swap using convolutional neural networks. In ICCV. 3677--3685.","DOI":"10.1109\/ICCV.2017.397"},{"key":"e_1_3_2_2_22_1","unstructured":"Kimin Lee Kibok Lee Honglak Lee and Jinwoo Shin. 2018. A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In NIPS. 7167--7177. Kimin Lee Kibok Lee Honglak Lee and Jinwoo Shin. 2018. A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In NIPS. 7167--7177."},{"key":"e_1_3_2_2_23_1","unstructured":"Dongze Li Wei Wang Hongxing Fan and Jing Dong. 2021. Exploring Adversarial Fake Images on Face Manifold. In CVPR. 5789--5798. Dongze Li Wei Wang Hongxing Fan and Jing Dong. 2021. Exploring Adversarial Fake Images on Face Manifold. In CVPR. 5789--5798."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Lingzhi Li Jianmin Bao Ting Zhang Hao Yang Dong Chen Fang Wen and Baining Guo. 2020a. Face x-ray for more general face forgery detection. In CVPR. 5001--5010. Lingzhi Li Jianmin Bao Ting Zhang Hao Yang Dong Chen Fang Wen and Baining Guo. 2020a. Face x-ray for more general face forgery detection. In CVPR. 5001--5010.","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Shasha Li Shitong Zhu Sudipta Paul Amit Roy-Chowdhury Chengyu Song Srikanth Krishnamurthy Ananthram Swami and Kevin S Chan. 2020b. Connecting the dots: Detecting adversarial perturbations using context inconsistency. In ECCV. 396--413. Shasha Li Shitong Zhu Sudipta Paul Amit Roy-Chowdhury Chengyu Song Srikanth Krishnamurthy Ananthram Swami and Kevin S Chan. 2020b. Connecting the dots: Detecting adversarial perturbations using context inconsistency. In ECCV. 396--413.","DOI":"10.1007\/978-3-030-58592-1_24"},{"key":"e_1_3_2_2_26_1","volume-title":"Hiding faces in plain sight: Disrupting ai face synthesis with adversarial perturbations. arXiv preprint arXiv:1906.09288","author":"Li Yuezun","year":"2019","unstructured":"Yuezun Li , Xin Yang , Baoyuan Wu , and Siwei Lyu . 2019. Hiding faces in plain sight: Disrupting ai face synthesis with adversarial perturbations. arXiv preprint arXiv:1906.09288 ( 2019 ). Yuezun Li, Xin Yang, Baoyuan Wu, and Siwei Lyu. 2019. Hiding faces in plain sight: Disrupting ai face synthesis with adversarial perturbations. arXiv preprint arXiv:1906.09288 (2019)."},{"key":"e_1_3_2_2_27_1","volume-title":"STGAN: A unified selective transfer network for arbitrary image attribute editing. In CVPR. 3673--3682.","author":"Liu Ming","year":"2019","unstructured":"Ming Liu , Yukang Ding , Min Xia , Xiao Liu , Errui Ding , Wangmeng Zuo , and Shilei Wen . 2019 . STGAN: A unified selective transfer network for arbitrary image attribute editing. In CVPR. 3673--3682. Ming Liu, Yukang Ding, Min Xia, Xiao Liu, Errui Ding, Wangmeng Zuo, and Shilei Wen. 2019. STGAN: A unified selective transfer network for arbitrary image attribute editing. In CVPR. 3673--3682."},{"key":"e_1_3_2_2_28_1","unstructured":"Xingjun Ma Bo Li Yisen Wang Sarah M Erfani Sudanthi Wijewickrema Grant Schoenebeck Dawn Song Michael E Houle and James Bailey. 2018. Characterizing adversarial subspaces using local intrinsic dimensionality. In ICLR. Xingjun Ma Bo Li Yisen Wang Sarah M Erfani Sudanthi Wijewickrema Grant Schoenebeck Dawn Song Michael E Houle and James Bailey. 2018. Characterizing adversarial subspaces using local intrinsic dimensionality. In ICLR."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"crossref","unstructured":"Scott McCloskey and Michael Albright. 2019. Detecting GAN-generated imagery using saturation cues. In ICIP. 4584--4588. Scott McCloskey and Michael Albright. 2019. Detecting GAN-generated imagery using saturation cues. In ICIP. 4584--4588.","DOI":"10.1109\/ICIP.2019.8803661"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2214050"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Paarth Neekhara Brian Dolhansky Joanna Bitton and Cristian Canton-Ferrer. 2021. Adversarial Threats to DeepFake Detection: A Practical Perspective. In CVPRW. 923--932. Paarth Neekhara Brian Dolhansky Joanna Bitton and Cristian Canton-Ferrer. 2021. Adversarial Threats to DeepFake Detection: A Practical Perspective. In CVPRW. 923--932.","DOI":"10.1109\/CVPRW53098.2021.00103"},{"key":"e_1_3_2_2_32_1","volume-title":"Stacked hourglass networks for human pose estimation","author":"Newell Alejandro","unstructured":"Alejandro Newell , Kaiyu Yang , and Jia Deng . 2016. Stacked hourglass networks for human pose estimation . In ECCV. Springer , 483--499. Alejandro Newell, Kaiyu Yang, and Jia Deng. 2016. Stacked hourglass networks for human pose estimation. In ECCV. Springer, 483--499."},{"key":"e_1_3_2_2_33_1","volume-title":"Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning. arXiv preprint arXiv:1803.04765","author":"Papernot Nicolas","year":"2018","unstructured":"Nicolas Papernot and Patrick McDaniel . 2018. Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning. arXiv preprint arXiv:1803.04765 ( 2018 ). Nicolas Papernot and Patrick McDaniel. 2018. Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning. arXiv preprint arXiv:1803.04765 (2018)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01210-3"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Hua Qi Qing Guo Felix Juefei-Xu Xiaofei Xie Lei Ma Wei Feng Yang Liu and Jianjun Zhao. 2020. DeepRhythm: Exposing deepfakes with attentional visual heartbeat rhythms. In ACMMM. 4318--4327. Hua Qi Qing Guo Felix Juefei-Xu Xiaofei Xie Lei Ma Wei Feng Yang Liu and Jianjun Zhao. 2020. DeepRhythm: Exposing deepfakes with attentional visual heartbeat rhythms. In ACMMM. 4318--4327.","DOI":"10.1145\/3394171.3413707"},{"key":"e_1_3_2_2_36_1","volume-title":"Faceforensics: Learning to detect manipulated facial images. In CVPR. 1--11.","author":"Rossler Andreas","year":"2019","unstructured":"Andreas Rossler , Davide Cozzolino , Luisa Verdoliva , Christian Riess , Justus Thies , and Matthias Nie\u00dfner . 2019 . Faceforensics: Learning to detect manipulated facial images. In CVPR. 1--11. Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nie\u00dfner. 2019. Faceforensics: Learning to detect manipulated facial images. In CVPR. 1--11."},{"key":"e_1_3_2_2_37_1","volume-title":"Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena","author":"Rudin Leonid I","year":"1992","unstructured":"Leonid I Rudin , Stanley Osher , and Emad Fatemi . 1992. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena , Vol. 60 , 1--4 ( 1992 ), 259--268. Leonid I Rudin, Stanley Osher, and Emad Fatemi. 1992. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena , Vol. 60, 1--4 (1992), 259--268."},{"key":"e_1_3_2_2_38_1","volume-title":"Sarah Adel Bargal, and Stan Sclaroff","author":"Ruiz Nataniel","year":"2020","unstructured":"Nataniel Ruiz , Sarah Adel Bargal, and Stan Sclaroff . 2020 . Disrupting deepfakes: Adversarial attacks against conditional image translation networks and facial manipulation systems. In ECCV. Springer , 236--251. Nataniel Ruiz, Sarah Adel Bargal, and Stan Sclaroff. 2020. Disrupting deepfakes: Adversarial attacks against conditional image translation networks and facial manipulation systems. In ECCV. Springer, 236--251."},{"key":"e_1_3_2_2_39_1","volume-title":"Fawkes: Protecting privacy against unauthorized deep learning models. In $$USENIX$$ Security Symposium. 1589--1604.","author":"Shan Shawn","year":"2020","unstructured":"Shawn Shan , Emily Wenger , Jiayun Zhang , Huiying Li , Haitao Zheng , and Ben Y Zhao . 2020 . Fawkes: Protecting privacy against unauthorized deep learning models. In $$USENIX$$ Security Symposium. 1589--1604. Shawn Shan, Emily Wenger, Jiayun Zhang, Huiying Li, Haitao Zheng, and Ben Y Zhao. 2020. Fawkes: Protecting privacy against unauthorized deep learning models. In $$USENIX$$ Security Symposium. 1589--1604."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"crossref","unstructured":"Yujun Shen Jinjin Gu Xiaoou Tang and Bolei Zhou. 2020. Interpreting the latent space of gans for semantic face editing. In CVPR. 9243--9252. Yujun Shen Jinjin Gu Xiaoou Tang and Bolei Zhou. 2020. Interpreting the latent space of gans for semantic face editing. In CVPR. 9243--9252.","DOI":"10.1109\/CVPR42600.2020.00926"},{"key":"e_1_3_2_2_41_1","unstructured":"Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In ICLR. Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In ICLR."},{"key":"e_1_3_2_2_42_1","unstructured":"Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna Dumitru Erhan Ian Goodfellow and Rob Fergus. 2014. Intriguing properties of neural networks. In ICLR. Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna Dumitru Erhan Ian Goodfellow and Rob Fergus. 2014. Intriguing properties of neural networks. In ICLR."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2831899"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"crossref","unstructured":"Justus Thies Michael Zollhofer Marc Stamminger Christian Theobalt and Matthias Nie\u00dfner. 2016. Face2face: Real-time face capture and reenactment of rgb videos. In CVPR. 2387--2395. Justus Thies Michael Zollhofer Marc Stamminger Christian Theobalt and Matthias Nie\u00dfner. 2016. Face2face: Real-time face capture and reenactment of rgb videos. In CVPR. 2387--2395.","DOI":"10.1109\/CVPR.2016.262"},{"key":"e_1_3_2_2_45_1","volume-title":"Gan inversion: A survey. TPAMI","author":"Xia Weihao","year":"2022","unstructured":"Weihao Xia , Yulun Zhang , Yujiu Yang , Jing-Hao Xue , Bolei Zhou , and Ming-Hsuan Yang . 2022. Gan inversion: A survey. TPAMI ( 2022 ). Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang. 2022. Gan inversion: A survey. TPAMI (2022)."},{"key":"e_1_3_2_2_46_1","volume-title":"Defending against gan-based deepfake attacks via transformation-aware adversarial faces","author":"Yang Chaofei","unstructured":"Chaofei Yang , Leah Ding , Yiran Chen , and Hai Li. 2021. Defending against gan-based deepfake attacks via transformation-aware adversarial faces . In IJCNN. IEEE , 1--8. Chaofei Yang, Leah Ding, Yiran Chen, and Hai Li. 2021. Defending against gan-based deepfake attacks via transformation-aware adversarial faces. In IJCNN. IEEE, 1--8."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"crossref","unstructured":"Xin Yang Yuezun Li and Siwei Lyu. 2019. Exposing deep fakes using inconsistent head poses. In ICASSP. 8261--8265. Xin Yang Yuezun Li and Siwei Lyu. 2019. Exposing deep fakes using inconsistent head poses. In ICASSP. 8261--8265.","DOI":"10.1109\/ICASSP.2019.8683164"},{"key":"e_1_3_2_2_48_1","unstructured":"Chin-Yuan Yeh Hsi-Wen Chen Shang-Lun Tsai and Sheng-De Wang. 2020. Disrupting image-translation-based deepfake algorithms with adversarial attacks. In WACVW. 53--62. Chin-Yuan Yeh Hsi-Wen Chen Shang-Lun Tsai and Sheng-De Wang. 2020. Disrupting image-translation-based deepfake algorithms with adversarial attacks. In WACVW. 53--62."},{"key":"e_1_3_2_2_49_1","unstructured":"Ning Yu Larry S Davis and Mario Fritz. 2019. Attributing fake images to gans: Learning and analyzing gan fingerprints. In ICCV. 7556--7566. Ning Yu Larry S Davis and Mario Fritz. 2019. Attributing fake images to gans: Learning and analyzing gan fingerprints. In ICCV. 7556--7566."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2018.2886771"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"crossref","unstructured":"Jianli Zhou Chao Liang and Jun Chen. 2020. Manifold Projection for Adversarial Defense on Face Recognition. In ECCV. 288--305. Jianli Zhou Chao Liang and Jun Chen. 2020. Manifold Projection for Adversarial Defense on Face Recognition. In ECCV. 288--305.","DOI":"10.1007\/978-3-030-58577-8_18"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"crossref","unstructured":"Peng Zhou Xintong Han Vlad I Morariu and Larry S Davis. 2017. Two-stream neural networks for tampered face detection. In CVPRW. 1831--1839. Peng Zhou Xintong Han Vlad I Morariu and Larry S Davis. 2017. Two-stream neural networks for tampered face detection. In CVPRW. 1831--1839.","DOI":"10.1109\/CVPRW.2017.229"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"crossref","unstructured":"Jiapeng Zhu Yujun Shen Deli Zhao and Bolei Zhou. 2020. In-domain gan inversion for real image editing. In ECCV. 592--608. Jiapeng Zhu Yujun Shen Deli Zhao and Bolei Zhou. 2020. In-domain gan inversion for real image editing. In ECCV. 592--608.","DOI":"10.1007\/978-3-030-58520-4_35"},{"key":"e_1_3_2_2_54_1","volume-title":"Generative visual manipulation on the natural image manifold","author":"Zhu Jun-Yan","unstructured":"Jun-Yan Zhu , Philipp Kr\"ahenb \u00fchl , Eli Shechtman , and Alexei A Efros . 2016. Generative visual manipulation on the natural image manifold . In ECCV. Springer , 597--613.io Jun-Yan Zhu, Philipp Kr\"ahenb\u00fchl, Eli Shechtman, and Alexei A Efros. 2016. Generative visual manipulation on the natural image manifold. In ECCV. Springer, 597--613.io"}],"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\/3503161.3547923","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3547923","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:31Z","timestamp":1750186831000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3547923"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":54,"alternative-id":["10.1145\/3503161.3547923","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3547923","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"}}]}}