Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
Face recognition has achieved tremendous success in both its theory and technology. However, with increasingly realistic attacks, such as print photos, replay videos, and 3D masks, as well as new attack methods like AI-generated faces or videos, face recognition systems are confronted with significa...
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| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6891 |
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| author | Huifen Xing Siok Yee Tan Faizan Qamar Yuqing Jiao |
| author_facet | Huifen Xing Siok Yee Tan Faizan Qamar Yuqing Jiao |
| author_sort | Huifen Xing |
| collection | DOAJ |
| description | Face recognition has achieved tremendous success in both its theory and technology. However, with increasingly realistic attacks, such as print photos, replay videos, and 3D masks, as well as new attack methods like AI-generated faces or videos, face recognition systems are confronted with significant challenges and risks. Distinguishing between real and fake faces, i.e., face anti-spoofing (FAS), is crucial to the security of face recognition systems. With the advent of large-scale academic datasets in recent years, FAS based on deep learning has achieved a remarkable level of performance and now dominates the field. This paper systematically reviews the latest advancements in FAS based on deep learning. First, it provides an overview of the background, basic concepts, and types of FAS attacks. Then, it categorizes existing FAS methods from the perspectives of RGB (red, green and blue) modality and other modalities, discussing the main concepts, the types of attacks that can be detected, their advantages and disadvantages, and so on. Next, it introduces popular datasets used in FAS research and highlights their characteristics. Finally, it summarizes the current research challenges and future directions for FAS, such as its limited generalization for unknown attacks, the insufficient multi-modal research, the spatiotemporal efficiency of algorithms, and unified detection for presentation attacks and deepfakes. We aim to provide a comprehensive reference in this field and to inspire progress within the FAS community, guiding researchers toward promising directions for future work. |
| format | Article |
| id | doaj-art-af874dc4f5674d0998db59db56d00923 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-af874dc4f5674d0998db59db56d009232025-08-20T03:26:21ZengMDPI AGApplied Sciences2076-34172025-06-011512689110.3390/app15126891Face Anti-Spoofing Based on Deep Learning: A Comprehensive SurveyHuifen Xing0Siok Yee Tan1Faizan Qamar2Yuqing Jiao3Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, MalaysiaCenter for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, MalaysiaCenter for Cyber Security, Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, MalaysiaCenter for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, MalaysiaFace recognition has achieved tremendous success in both its theory and technology. However, with increasingly realistic attacks, such as print photos, replay videos, and 3D masks, as well as new attack methods like AI-generated faces or videos, face recognition systems are confronted with significant challenges and risks. Distinguishing between real and fake faces, i.e., face anti-spoofing (FAS), is crucial to the security of face recognition systems. With the advent of large-scale academic datasets in recent years, FAS based on deep learning has achieved a remarkable level of performance and now dominates the field. This paper systematically reviews the latest advancements in FAS based on deep learning. First, it provides an overview of the background, basic concepts, and types of FAS attacks. Then, it categorizes existing FAS methods from the perspectives of RGB (red, green and blue) modality and other modalities, discussing the main concepts, the types of attacks that can be detected, their advantages and disadvantages, and so on. Next, it introduces popular datasets used in FAS research and highlights their characteristics. Finally, it summarizes the current research challenges and future directions for FAS, such as its limited generalization for unknown attacks, the insufficient multi-modal research, the spatiotemporal efficiency of algorithms, and unified detection for presentation attacks and deepfakes. We aim to provide a comprehensive reference in this field and to inspire progress within the FAS community, guiding researchers toward promising directions for future work.https://www.mdpi.com/2076-3417/15/12/6891face anti-spoofingpresentation attacksdeep learningmulti-modaldomain generalization |
| spellingShingle | Huifen Xing Siok Yee Tan Faizan Qamar Yuqing Jiao Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey Applied Sciences face anti-spoofing presentation attacks deep learning multi-modal domain generalization |
| title | Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey |
| title_full | Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey |
| title_fullStr | Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey |
| title_full_unstemmed | Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey |
| title_short | Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey |
| title_sort | face anti spoofing based on deep learning a comprehensive survey |
| topic | face anti-spoofing presentation attacks deep learning multi-modal domain generalization |
| url | https://www.mdpi.com/2076-3417/15/12/6891 |
| work_keys_str_mv | AT huifenxing faceantispoofingbasedondeeplearningacomprehensivesurvey AT siokyeetan faceantispoofingbasedondeeplearningacomprehensivesurvey AT faizanqamar faceantispoofingbasedondeeplearningacomprehensivesurvey AT yuqingjiao faceantispoofingbasedondeeplearningacomprehensivesurvey |