Forgery face detection method based on multi-domain temporal features mining
Financial technology has greatly facilitated people’s daily life with the continuous development of computer technology in the financial services industry.However, digital finance is accompanied by security problems that can be extremely harmful.Face biometrics, as an important part of identity info...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2023-06-01
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| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023044 |
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| _version_ | 1841529631385059328 |
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| author | Chuntao ZHU Chengxi YIN Bolin ZHANG Qilin YIN Wei LU |
| author_facet | Chuntao ZHU Chengxi YIN Bolin ZHANG Qilin YIN Wei LU |
| author_sort | Chuntao ZHU |
| collection | DOAJ |
| description | Financial technology has greatly facilitated people’s daily life with the continuous development of computer technology in the financial services industry.However, digital finance is accompanied by security problems that can be extremely harmful.Face biometrics, as an important part of identity information, is widely used in payment systems, account registration, and many other aspects of the financial industry.The emergence of face forgery technology constantly impacts the digital financial security system, posing a threat to national asset security and social stability.To address the security problems caused by fake faces, a forgery face detection method based on multi-domain temporal features mining was proposed.The tampering features were distinguished and enhanced based on the consistency of statistical feature data distribution and temporal action trend in the temporal features of videos existing in the spatial domain and frequency domain.Temporal information was mined in the spatial domain using an improved LSTM, while in the frequency domain, temporal information existing in different frequency bands of the spectrum was mined using 3D convolution layers.The information was then fused with the tampering features extracted from the backbone network, thus effectively distinguishing forged faces from real ones.The effectiveness of the proposed method was demonstrated on mainstream datasets. |
| format | Article |
| id | doaj-art-9e5498e9cbe647aa8f67f1becc18c77b |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2023-06-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-9e5498e9cbe647aa8f67f1becc18c77b2025-01-15T03:16:39ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2023-06-01912313459578529Forgery face detection method based on multi-domain temporal features miningChuntao ZHUChengxi YINBolin ZHANGQilin YINWei LUFinancial technology has greatly facilitated people’s daily life with the continuous development of computer technology in the financial services industry.However, digital finance is accompanied by security problems that can be extremely harmful.Face biometrics, as an important part of identity information, is widely used in payment systems, account registration, and many other aspects of the financial industry.The emergence of face forgery technology constantly impacts the digital financial security system, posing a threat to national asset security and social stability.To address the security problems caused by fake faces, a forgery face detection method based on multi-domain temporal features mining was proposed.The tampering features were distinguished and enhanced based on the consistency of statistical feature data distribution and temporal action trend in the temporal features of videos existing in the spatial domain and frequency domain.Temporal information was mined in the spatial domain using an improved LSTM, while in the frequency domain, temporal information existing in different frequency bands of the spectrum was mined using 3D convolution layers.The information was then fused with the tampering features extracted from the backbone network, thus effectively distinguishing forged faces from real ones.The effectiveness of the proposed method was demonstrated on mainstream datasets.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023044face authenticationDeepfake detectiontemporal featuresmulti-domain features |
| spellingShingle | Chuntao ZHU Chengxi YIN Bolin ZHANG Qilin YIN Wei LU Forgery face detection method based on multi-domain temporal features mining 网络与信息安全学报 face authentication Deepfake detection temporal features multi-domain features |
| title | Forgery face detection method based on multi-domain temporal features mining |
| title_full | Forgery face detection method based on multi-domain temporal features mining |
| title_fullStr | Forgery face detection method based on multi-domain temporal features mining |
| title_full_unstemmed | Forgery face detection method based on multi-domain temporal features mining |
| title_short | Forgery face detection method based on multi-domain temporal features mining |
| title_sort | forgery face detection method based on multi domain temporal features mining |
| topic | face authentication Deepfake detection temporal features multi-domain features |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023044 |
| work_keys_str_mv | AT chuntaozhu forgeryfacedetectionmethodbasedonmultidomaintemporalfeaturesmining AT chengxiyin forgeryfacedetectionmethodbasedonmultidomaintemporalfeaturesmining AT bolinzhang forgeryfacedetectionmethodbasedonmultidomaintemporalfeaturesmining AT qilinyin forgeryfacedetectionmethodbasedonmultidomaintemporalfeaturesmining AT weilu forgeryfacedetectionmethodbasedonmultidomaintemporalfeaturesmining |