Robustness evaluation of commercial liveness detection platform
Liveness detection technology has become an important application in daily life, and it is used in scenarios including mobile phone face unlock, face payment, and remote authentication.However, if attackers use fake video generation technology to generate realistic face-swapping videos to attack the...
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| Format: | Article |
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POSTS&TELECOM PRESS Co., LTD
2022-02-01
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| Series: | 网络与信息安全学报 |
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| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2022010 |
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| author | Pengcheng WANG Haibin ZHENG Jianfei ZOU Ling PANG Hu LI Jinyin CHEN |
| author_facet | Pengcheng WANG Haibin ZHENG Jianfei ZOU Ling PANG Hu LI Jinyin CHEN |
| author_sort | Pengcheng WANG |
| collection | DOAJ |
| description | Liveness detection technology has become an important application in daily life, and it is used in scenarios including mobile phone face unlock, face payment, and remote authentication.However, if attackers use fake video generation technology to generate realistic face-swapping videos to attack the living body detection system in the above scenarios, it will pose a huge threat to the security of these scenarios.Aiming at this problem, four state-of-the-art Deepfake technologies were used to generate a large number of face-changing pictures and videos as test samples, and use these samples to test the online API interfaces of commercial live detection platforms such as Baidu and Tencent.The test results show that the detection success rate of Deepfake images is generally very low by the major commercial live detection platforms currently used, and they are more sensitive to the quality of images, and the false detection rate of real images is also high.The main reason for the analysis may be that these platforms were mainly designed for traditional living detection attack methods such as printing photo attacks, screen remake attacks, and silicone mask attacks, and did not integrate advanced face-changing detection technology into their liveness detection.In the algorithm, these platforms cannot effectively deal with Deepfake attacks.Therefore, an integrated live detection method Integranet was proposed, which was obtained by integrating four detection algorithms for different image features.It could effectively detect traditional attack methods such as printed photos and screen remakes.It could also effectively detect against advanced Deepfake attacks.The detection effect of Integranet was verified on the test data set.The results show that the detection success rate of Deepfake images by proposed Integranet detection method is at least 35% higher than that of major commercial live detection platforms. |
| format | Article |
| id | doaj-art-24f861ba86d74cf28167ed6bee6ed76c |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2022-02-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-24f861ba86d74cf28167ed6bee6ed76c2025-01-15T03:15:43ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2022-02-01818018959572070Robustness evaluation of commercial liveness detection platformPengcheng WANGHaibin ZHENGJianfei ZOULing PANGHu LIJinyin CHENLiveness detection technology has become an important application in daily life, and it is used in scenarios including mobile phone face unlock, face payment, and remote authentication.However, if attackers use fake video generation technology to generate realistic face-swapping videos to attack the living body detection system in the above scenarios, it will pose a huge threat to the security of these scenarios.Aiming at this problem, four state-of-the-art Deepfake technologies were used to generate a large number of face-changing pictures and videos as test samples, and use these samples to test the online API interfaces of commercial live detection platforms such as Baidu and Tencent.The test results show that the detection success rate of Deepfake images is generally very low by the major commercial live detection platforms currently used, and they are more sensitive to the quality of images, and the false detection rate of real images is also high.The main reason for the analysis may be that these platforms were mainly designed for traditional living detection attack methods such as printing photo attacks, screen remake attacks, and silicone mask attacks, and did not integrate advanced face-changing detection technology into their liveness detection.In the algorithm, these platforms cannot effectively deal with Deepfake attacks.Therefore, an integrated live detection method Integranet was proposed, which was obtained by integrating four detection algorithms for different image features.It could effectively detect traditional attack methods such as printed photos and screen remakes.It could also effectively detect against advanced Deepfake attacks.The detection effect of Integranet was verified on the test data set.The results show that the detection success rate of Deepfake images by proposed Integranet detection method is at least 35% higher than that of major commercial live detection platforms.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2022010Deepfakeliveness detectioncommercial platformrobustness |
| spellingShingle | Pengcheng WANG Haibin ZHENG Jianfei ZOU Ling PANG Hu LI Jinyin CHEN Robustness evaluation of commercial liveness detection platform 网络与信息安全学报 Deepfake liveness detection commercial platform robustness |
| title | Robustness evaluation of commercial liveness detection platform |
| title_full | Robustness evaluation of commercial liveness detection platform |
| title_fullStr | Robustness evaluation of commercial liveness detection platform |
| title_full_unstemmed | Robustness evaluation of commercial liveness detection platform |
| title_short | Robustness evaluation of commercial liveness detection platform |
| title_sort | robustness evaluation of commercial liveness detection platform |
| topic | Deepfake liveness detection commercial platform robustness |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2022010 |
| work_keys_str_mv | AT pengchengwang robustnessevaluationofcommerciallivenessdetectionplatform AT haibinzheng robustnessevaluationofcommerciallivenessdetectionplatform AT jianfeizou robustnessevaluationofcommerciallivenessdetectionplatform AT lingpang robustnessevaluationofcommerciallivenessdetectionplatform AT huli robustnessevaluationofcommerciallivenessdetectionplatform AT jinyinchen robustnessevaluationofcommerciallivenessdetectionplatform |