面向商用活体检测平台的鲁棒性评估

doi:10.11959/j.issn.2096-109x.2022010

Abstract

Abstract:

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.

Key words: Deepfake, liveness detection, commercial platform, robustness

CLC Number:

TP391

Pengcheng WANG, Haibin ZHENG, Jianfei ZOU, Ling PANG, Hu LI, Jinyin CHEN. Robustness evaluation of commercial liveness detection platform[J]. Chinese Journal of Network and Information Security, 2022, 8(1): 180-189.

Figures/Tables 10

平台	Integranet	科大讯飞	腾讯	百度	阿里巴巴	京东	云从科技
FaceSwap	$2 . 12 %$	41.23%	78.26%	83.42%	86.26%	98.19%	99.34%
FaceShift	$10 . 34 %$	95.24%	61.41%	61.19%	85.39%	97.38%	99.42%
FSGAN	$9 . 68 %$	63.14%	81.26%	85.27%	85.24%	98.17%	99.29%
FOMM	$7 . 41 %$	97.29%	80.31%	90.18%	99.17%	99.42%	99.14%

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商用平台	输入格式	应用接口
阿里巴巴	图片	活体检测
百度	图片、视频	活体检测、伪造检测
科大讯飞	图片	活体检测
商汤科技	图片、视频	活体检测、伪造检测
腾讯	图片、视频	活体检测
云从科技	图片	活体检测
京东	图片	活体检测

压缩方法	压缩前通过率	压缩后通过率
SVD	41%	85%
k-means聚类	41%	21%
格式转换	41%	66%

Robustness evaluation of commercial liveness detection platform

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