{"type":"ScholarlyArticle","title":"Unified Physical-Digital Face Attack Detection","authors":[{"name":"Hao Fang","slug":"hao-fang","given":"Hao","family":"Fang","affiliation":"MAIS , Institute of Automation of Chinese Academy of Sciences , Beijing , China"},{"name":"Ajian Liu","slug":"ajian-liu","given":"Ajian","family":"Liu","affiliation":"MAIS , Institute of Automation of Chinese Academy of Sciences , Beijing , China"},{"name":"Haocheng Yuan","slug":"haocheng-yuan","given":"Haocheng","family":"Yuan","affiliation":"Macau University of Science and Technology (MUST) , Macau , China"},{"name":"Junze Zheng","slug":"junze-zheng","given":"Junze","family":"Zheng","affiliation":"Macau University of Science and Technology (MUST) , Macau , China"},{"name":"Dingheng Zeng","slug":"dingheng-zeng","given":"Dingheng","family":"Zeng","affiliation":"Mashang Consumer Finance Co., Ltd. , Chongqing , China"},{"name":"Yanhong Liu","slug":"yanhong-liu","given":"Yanhong","family":"Liu","affiliation":"Mashang Consumer Finance Co., Ltd. , Chongqing , China"},{"name":"Jiankang Deng","slug":"jiankang-deng","given":"Jiankang","family":"Deng","affiliation":"Imperial College London , London , UK"},{"name":"Sergio Escalera","slug":"sergio-escalera","given":"Sergio","family":"Escalera","affiliation":"Computer Vision Center (CVC) , Barcelona , Catalonia , Spain"},{"name":"Xiaoming Liu","slug":"xiaoming-liu","given":"Xiaoming","family":"Liu","affiliation":"Department of Computer Science and Engineering , Michigan State University"},{"name":"Jun Wan","slug":"jun-wan","given":"Jun","family":"Wan","affiliation":"MAIS , Institute of Automation of Chinese Academy of Sciences , Beijing , China; Macau University of Science and Technology (MUST) , Macau , China"},{"name":"Zhen Lei","slug":"zhen-lei","given":"Zhen","family":"Lei","affiliation":"MAIS , Institute of Automation of Chinese Academy of Sciences , Beijing , China; CAIR Hong Kong Institute of Science & Innovation , Chinese Academy of Sciences"}],"abstract":"Face Recognition (FR) systems can suffer from physical (i.e., print photo) and digital (i.e., DeepFake) attacks. However, previous related work rarely considers both situations at the same time. This implies the deployment of multiple models and thus more computational burden. The main reasons for this lack of an integrated model are caused by two factors: (1) The lack of a dataset including both physical and digital attacks which the same ID covers the real face and all attack types; (2) Given the large intra-class variance between these two attacks, it is difficult to learn a compact feature space to detect both attacks simultaneously. To address these issues, we collect a Unified physical-digital Attack dataset, called UniAttackData. The dataset consists of 1,800 participations of 2 and 12 physical and digital attacks, respectively, resulting in a total of 28,706 videos. Then, we propose a Unified Attack Detection framework based on Vision-Language Models (VLMs), namely UniAttackDetection, which includes three main modules: the Teacher-Student Prompts (TSP) module, focused on acquiring unified and specific knowledge respectively; the Unified Knowledge Mining (UKM) module, designed to capture a comprehensive feature space; and the Sample-Level Prompt Interaction (SLPI) module, aimed at grasping sample-level semantics. These three modules seamlessly form a robust unified attack detection framework. Extensive experiments on UniAttackData and three other datasets demonstrate the superiority of our approach for unified face attack detection. Dataset link: https://sites.google.com/view/face-anti-spoofing-challenge/dataset-download/uniattackdatacvpr2024","doi":"10.24963/ijcai.2024/83","url":"https://ijcai.org/proceedings/2024/83","pdf":"https://ijcai.org/proceedings/2024/0083.pdf","markdown":"https://ijcai.org/proceedings/2024/83.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/83","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":"Computer Vision","track":"Main Track","number":83,"pages":{"first":749,"last":757},"keywords":["Machine Learning: ML: Multi-modal learning","Computer Vision: CV: Biometrics, face, gesture and pose recognition"],"videos":[],"references":[{"position":1,"raw":"[Boulkenafet et al., 2017] Zinelabinde Boulkenafet, Jukka Komulainen, Lei Li, Xiaoyi Feng, and Abdenour Hadid. Oulu-npu: A mobile face presentation attack database with real-world variations. In FG, pages 612-618, 2017.","title":"Oulu-npu: A mobile face presentation attack database with real-world variations","authors":"Boulkenafet","year":"2017","venue":"FG","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2020] Renwang Chen, Xuanhong Chen, Bing- bing Ni, and Yanhao Ge. Simswap: An efficient frame- work for high fidelity face swapping. In ACM MM, pages 2003-2011, 2020.","title":"Simswap: An efficient framework for high fidelity face swapping","authors":"Chen","year":"2020","venue":"ACM MM","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Choi et al., 2018] Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. Star- gan: Unified generative adversarial networks for multi- domain image-to-image translation, 2018.","title":"Stargan: Unified generative adversarial networks for multidomain image-to-image translation","authors":"Choi","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Dabouei et al., 2019] Ali Dabouei, Sobhan Soley- mani, Jeremy Dawson, and Nasser Nasrabadi. Fast geometrically-perturbed adversarial faces. In WACV, pages 1979-1988. IEEE, 2019.","title":"Fast geometrically-perturbed adversarial faces","authors":"Ali Dabouei, Sobhan Soleymani, Jeremy Dawson, Nasser Nasrabadi","year":"2019","venue":"WACV","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Dang et al., 2020] Hao Dang, Feng Liu, Joel Stehouwer, Xiaoming Liu, and Anil K Jain. On the detection of digital face manipulation. In CVPR, pages 5781-5790, 2020.","title":"On the detection of digital face manipulation","authors":"Hao Dang, Feng Liu, Joel Stehouwer, Xiaoming Liu, Anil K Jain","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Deb et al., 2019] Debayan Deb, Jianbang Zhang, and Anil K. Jain. Advfaces: Adversarial face synthesis, 2019.","title":"Advfaces: Adversarial face synthesis","authors":"Deb","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Deb et al., 2023] Debayan Deb, Xiaoming Liu, and Anil K Jain. Unified detection of digital and physical face attacks. In FG, pages 1-8. IEEE, 2023.","title":"Unified detection of digital and physical face attacks","authors":"Deb","year":"2023","venue":"FG","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Ding et al., 2020] Xinyi Ding, Zohreh Raziei, Eric C Lar- son, Eli V Olinick, Paul Krueger, and Michael Hahsler. Swapped face detection using deep learning and subjec- tive assessment. EURASIP JIS, pages 1-12, 2020.","title":"Swapped face detection using deep learning and subjective assessment","authors":"Xinyi Ding, Zohreh Raziei, Eric C Larson, Eli V Olinick, Paul Krueger, Michael Hahsler","year":"2020","venue":"EURASIP JIS","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Duan et al., 2021] Ranjie Duan, Yuefeng Chen, Dantong Niu, Yun Yang, A Kai Qin, and Yuan He. Advdrop: Ad- versarial attack to dnns by dropping information. In ICCV, pages 7506-7515, 2021.","title":"Advdrop: Adversarial attack to dnns by dropping information","authors":"Duan","year":"2021","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Fang et al., 2023] Hao Fang, Ajian Liu, Jun Wan, Sergio Escalera, Chenxu Zhao, Xu Zhang, Stan Z Li, and Zhen Lei. Surveillance face anti-spoofing. TIFS, 2023.","title":"Surveillance face anti-spoofing","authors":"Ajian Hao Fang, Jun Liu, Sergio Wan, Chenxu Escalera, Xu Zhao, Stan Z Zhang, Zhen Li, Lei","year":"2023","venue":"TIFS","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Goodfellow et al., 2014] Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. CoRR, abs/1412.6572, 2014.","title":"Explaining and harnessing adversarial examples","authors":"Ian J Goodfellow, Jonathon Shlens, Christian Szegedy","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Heusch et al., 2020] G. Heusch, A. George, D. Geissbuhler, Z. Mostaani, and S. Marcel. Deep models and shortwave infrared information to detect face presentation attacks. TBIOM, pages 1-1, 2020.","title":"Deep models and shortwave infrared information to detect face presentation attacks","authors":"G Heusch, A George, D Geissbuhler, Z Mostaani, S Marcel","year":"2020","venue":"TBIOM","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hong et al., 2022] Fa-Ting Hong, Longhao Zhang, Li Shen, and Dan Xu. Depth-aware generative adversarial network for talking head video generation. In CVPR, 2022.","title":"Depth-aware generative adversarial network for talking head video generation","authors":"Fa-Ting Hong, Longhao Zhang, Li Shen, Dan Xu","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Karras et al., 2020] Tero Karras, Samuli Laine, Miika Ait- tala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. An- alyzing and improving the image quality of StyleGAN. In CVPR, 2020.","title":"Analyzing and improving the image quality of StyleGAN","authors":"Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Khattak et al., 2023] Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, and Fa- had Shahbaz Khan. Maple: Multi-modal prompt learning. In CVPR, pages 19113-19122, 2023.","title":"Maple: Multi-modal prompt learning","authors":"Khattak","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Korshunov and Marcel, 2018] Pavel Korshunov and Se- bastien Marcel. Deepfakes: a new threat to face recogni- tion? assessment and detection. arXiv:1812.08685, 2018.","title":"Deepfakes: a new threat to face recognition? assessment and detection","authors":"Marcel ; Korshunov, Pavel Korshunov, Sebastien Marcel","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kowalski, 2018] Marek Kowalski. Faceswap github. Faceswap github\",[online] Available: https://github. com/MarekKowalski/FaceSwap, 2018.","title":"Faceswap github. Faceswap github","authors":"Marek Kowalski","year":null,"venue":null,"doi":null,"url":"https://github.com/MarekKowalski/FaceSwap,2018","internal_url":null},{"position":18,"raw":"[Li et al., 2018] Haoliang Li, Wen Li, Hong Cao, Shiqi Wang, Feiyue Huang, and Alex C Kot. Unsupervised do- main adaptation for face anti-spoofing. TIFS, 2018.","title":"Unsupervised domain adaptation for face anti-spoofing","authors":"Haoliang Li, Wen Li, Hong Cao, Shiqi Wang, Feiyue Huang, Alex C Kot","year":"2018","venue":"TIFS","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Liu and Liang, 2022] Ajian Liu and Yanyan Liang. Ma- vit: Modality-agnostic vision transformers for face anti- spoofing. In IJCAI, pages 1180-1186, 2022.","title":"Mavit: Modality-agnostic vision transformers for face antispoofing","authors":"Liang Liu, Ajian Liu, Yanyan Liang","year":"2022","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Liu et al., 2016] Siqi Liu, Pong C Yuen, Shengping Zhang, and Guoying Zhao. 3d mask face anti-spoofing with re- mote photoplethysmography. In ECCV, pages 85-100. Springer, 2016.","title":"3d mask face anti-spoofing with remote photoplethysmography","authors":"Siqi Liu, Shengping Pong C Yuen, Guoying Zhang, Zhao","year":"2016","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liu et al., 2018] Yaojie Liu, Amin Jourabloo, and Xiaom- ing Liu. Learning deep models for face anti-spoofing: Bi- nary or auxiliary supervision. In CVPR, 2018.","title":"Learning deep models for face anti-spoofing: Binary or auxiliary supervision","authors":"Liu","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Liu et al., 2019a] Ming Liu, Yukang Ding, Min Xia, Xiao Liu, Errui Ding, Wangmeng Zuo, and Shilei Wen. Stgan: A unified selective transfer network for arbitrary image at- tribute editing. In CVPR, pages 3673-3682, 2019.","title":"Stgan: A unified selective transfer network for arbitrary image attribute editing","authors":"Liu","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Liu et al., 2019b] Yaojie Liu, Joel Stehouwer, Amin Jourabloo, and Xiaoming Liu. Deep tree learning for zero-shot face anti-spoofing. In CVPR, 2019.","title":"Deep tree learning for zero-shot face anti-spoofing","authors":"Liu","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Liu et al., 2021a] Ajian Liu, Zichang Tan, Jun Wan, Ser- gio Escalera, Guodong Guo, and Stan Z Li. Casia-surf cefa: A benchmark for multi-modal cross-ethnicity face anti-spoofing. In WACV, pages 1179-1187, 2021.","title":"Casia-surf cefa: A benchmark for multi-modal cross-ethnicity face anti-spoofing","authors":"Liu","year":"2021","venue":"WACV","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Liu et al., 2021b] Ajian Liu, Zichang Tan, Jun Wan, Yanyan Liang, Zhen Lei, Guodong Guo, and Stan Z Li. Face anti- spoofing via adversarial cross-modality translation. TIFS, pages 2759-2772, 2021.","title":"Face antispoofing via adversarial cross-modality translation","authors":"Liu","year":"2021","venue":"TIFS","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Liu et al., 2022a] Ajian Liu, Jun Wan, Ning Jiang, Hongbin Wang, and Yanyan Liang. Disentangling facial pose and appearance information for face anti-spoofing. In ICPR, pages 4537-4543. IEEE, 2022.","title":"Disentangling facial pose and appearance information for face anti-spoofing","authors":"Liu","year":"2022","venue":"ICPR","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Liu et al., 2022b] Ajian Liu, Chenxu Zhao, Zitong Yu, Jun Wan, Anyang Su, Xing Liu, Zichang Tan, Sergio Escalera, Junliang Xing, Yanyan Liang, et al. Contrastive context- aware learning for 3d high-fidelity mask face presentation attack detection. TIFS, pages 2497-2507, 2022.","title":"Contrastive contextaware learning for 3d high-fidelity mask face presentation attack detection","authors":"Liu","year":"2022","venue":"TIFS","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Liu et al., 2023a] Ajian Liu, Zichang Tan, Yanyan Liang, and Jun Wan. Attack-agnostic deep face anti-spoofing. In CVPR Workshops, pages 6335-6344, 2023.","title":"Attack-agnostic deep face anti-spoofing","authors":"Liu","year":"2023","venue":"CVPR Workshops","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Liu et al., 2023b] Ajian Liu, Zichang Tan, Zitong Yu, Chenxu Zhao, Jun Wan, Yanyan Liang, Zhen Lei, Du Zhang, S. Li, and Guodong Guo. Fm-vit: Flexible modal vision transformers for face anti-spoofing. TIFS, pages 4775-4786, 2023.","title":"Fm-vit: Flexible modal vision transformers for face anti-spoofing","authors":"Liu","year":"2023","venue":"TIFS","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Luo et al., 2022] Cheng Luo, Qinliang Lin, Weicheng Xie, Bizhu Wu, Jinheng Xie, and Linlin Shen. Frequency- driven imperceptible adversarial attack on semantic sim- ilarity. In CVPR, pages 15315-15324, 2022.","title":"Frequencydriven imperceptible adversarial attack on semantic similarity","authors":"Cheng Luo, Qinliang Lin, Weicheng Xie, Bizhu Wu, Jinheng Xie, Linlin Shen","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Madry et al., 2019] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks, 2019.","title":"Towards deep learning models resistant to adversarial attacks","authors":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Moosavi-Dezfooli et al., 2016] Seyed-Mohsen Moosavi- Dezfooli, Alhussein Fawzi, and Pascal Frossard. Deep- fool: a simple and accurate method to fool deep neural networks, 2016.","title":"Deepfool: a simple and accurate method to fool deep neural networks","authors":"Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Pascal Frossard","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Qiu et al., 2020] Haonan Qiu, Chaowei Xiao, Lei Yang, Xinchen Yan, Honglak Lee, and Bo Li. Semanticadv: Generating adversarial examples via attribute-conditional image editing, 2020.","title":"Semanticadv: Generating adversarial examples via attribute-conditional image editing","authors":"Qiu","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Radford et al., 2021] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agar- wal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from nat- ural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021.","title":"Learning transferable visual models from natural language supervision","authors":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark","year":"2021","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Rony et al., 2021] Jérôme Rony, Eric Granger, Marco Ped- ersoli, and Ismail Ben Ayed. Augmented lagrangian ad- versarial attacks. In ICCV, pages 7738-7747, 2021.","title":"Augmented lagrangian adversarial attacks","authors":"Jérôme Rony, Eric Granger, Marco Pedersoli, Ismail Ben, Ayed","year":"2021","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Rosberg et al., 2023] Felix Rosberg, Eren Erdal Aksoy, Fernando Alonso-Fernandez, and Cristofer Englund. Facedancer: pose-and occlusion-aware high fidelity face swapping. In WACV, pages 3454-3463, 2023.","title":"Facedancer: pose-and occlusion-aware high fidelity face swapping","authors":"Felix Rosberg, Eren Erdal Aksoy, Fernando Alonso-Fernandez, Cristofer Englund","year":"2023","venue":"WACV","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Rossler et al., 2019] Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics++: Learning to detect manipulated facial images. In ICCV, pages 1-11, 2019.","title":"Faceforensics++: Learning to detect manipulated facial images","authors":"Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, Matthias Nießner","year":"2019","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Thies et al., 2016] Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, and Matthias Nießner. Face2face: Real-time face capture and reenactment of rgb videos. In CVPR, pages 2387-2395, 2016.","title":"Face2face: Real-time face capture and reenactment of rgb videos","authors":"Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, Matthias Nießner","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Thies et al., 2019] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image syn- thesis using neural textures. Acm TOG, pages 1-12, 2019.","title":"Deferred neural rendering: Image synthesis using neural textures","authors":"Justus Thies, Michael Zollhöfer, Matthias Nießner","year":"2019","venue":"Acm TOG","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wan et al., 2023] Jun Wan, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, and Stan Z Li. Advances in Face Presentation Attack Detection. Springer, 2023.","title":"Advances in Face Presentation Attack Detection","authors":"Wan","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Wang et al., 2021a] Qiulin Wang, Lu Zhang, and Bo Li. Safa: Structure aware face animation. In 3DV, pages 679- 688, 2021.","title":"Safa: Structure aware face animation","authors":"Wang","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Wang et al., 2021b] Ting-Chun Wang, Arun Mallya, and Ming-Yu Liu. One-shot free-view neural talking-head syn- thesis for video conferencing. In CVPR, pages 10039- 10049, 2021.","title":"One-shot free-view neural talking-head synthesis for video conferencing","authors":"Wang","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Wang et al., 2021c] Yajie Wang, Shangbo Wu, Wenyi Jiang, Shengang Hao, Yu-an Tan, and Quanxin Zhang. Demigu- ise attack: Crafting invisible semantic adversarial per- turbations with perceptual similarity. arXiv:2107.01396, 2021.","title":"Demiguise attack: Crafting invisible semantic adversarial perturbations with perceptual similarity","authors":"Wang","year":"2021","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2021/430"},{"position":44,"raw":"[Wang et al., 2022] Zhuo Wang, Zezheng Wang, Zitong Yu, Weihong Deng, Jiahong Li, Tingting Gao, and Zhongyuan Wang. Domain generalization via shuffled style assembly for face anti-spoofing. In CVPR, pages 4123-4133, 2022.","title":"Domain generalization via shuffled style assembly for face anti-spoofing","authors":"Zhuo Wang, Zezheng Wang, Zitong Yu, Weihong Deng, Jiahong Li, Tingting Gao, Zhongyuan Wang","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Wen et al., 2015] Di Wen, Hu Han, and Anil K Jain. Face spoof detection with image distortion analysis. TIFS, pages 746-761, 2015.","title":"Face spoof detection with image distortion analysis","authors":"Di Wen, Hu Han, Anil K Jain","year":"2015","venue":"TIFS","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Yan et al., 2022] Chiu Wai Yan, Tsz-Him Cheung, and Dit- Yan Yeung. Ila-da: Improving transferability of interme- diate level attack with data augmentation. In ICLR, 2022.","title":"Ila-da: Improving transferability of intermediate level attack with data augmentation","authors":"Tsz-Him Chiu Wai Yan, Dit-Yan Cheung, Yeung","year":"2022","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Yu et al., 2020a] Zitong Yu, Jun Wan, Yunxiao Qin, Xiaobai Li, Stan Z. Li, and Guoying Zhao. Nas-fas: Static-dynamic central difference network search for face anti-spoofing. In TPAMI, 2020.","title":"Nas-fas: Static-dynamic central difference network search for face anti-spoofing","authors":"Yu","year":"2020","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Yu et al., 2020b] Zitong Yu, Chenxu Zhao, Zezheng Wang, Yunxiao Qin, Zhuo Su, Xiaobai Li, Feng Zhou, and Guoy- ing Zhao. Searching central difference convolutional net- works for face anti-spoofing. In CVPR, pages 5295-5305, 2020.","title":"Searching central difference convolutional networks for face anti-spoofing","authors":"Yu","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Yu et al., 2022] Zitong Yu, Rizhao Cai, Zhi Li, Wenhan Yang, Jingang Shi, and Alex C Kot. Benchmarking joint face spoofing and forgery detection with visual and physi- ological cues. arXiv:2208.05401, 2022.","title":"Benchmarking joint face spoofing and forgery detection with visual and physiological cues","authors":"Yu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Zhao et al., 2021] Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, and Nenghai Yu. Multi-attentional deepfake detection. In CVPR, pages 2185-2194, 2021.","title":"Multi-attentional deepfake detection","authors":"Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, Nenghai Yu","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Zhou et al., 2023] Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Xuequan Lu, Ran Yi, Shouhong Ding, and Lizhuang Ma. Instance-aware domain generalization for face anti- spoofing. In CVPR, pages 20453-20463, 2023.","title":"Instance-aware domain generalization for face antispoofing","authors":"Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Xuequan Lu, Ran Yi, Shouhong Ding, Lizhuang Ma","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Zi et al., 2020] Bojia Zi, Minghao Chang, Jingjing Chen, Xingjun Ma, and Yu-Gang Jiang. Wilddeepfake: A chal- lenging real-world dataset for deepfake detection. In ACM MM, pages 2382-2390, 2020.","title":"Wilddeepfake: A challenging real-world dataset for deepfake detection","authors":"Zi","year":"2020","venue":"ACM MM","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Zou et al., 2022] Junhua Zou, Yexin Duan, Boyu Li, Wu Zhang, Yu Pan, and Zhisong Pan. Making adversar- ial examples more transferable and indistinguishable. In AAAI, volume 36, pages 3662-3670, 2022.","title":"Making adversarial examples more transferable and indistinguishable","authors":"Junhua Zou, Yexin Duan, Boyu Li, Wu Zhang, Yu Pan, Zhisong Pan","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}