面向商用活体检测平台的鲁棒性评估

Robustness evaluation of commercial liveness detection platform

活体检测技术已经成为日常生活中的重要应用,手机刷脸解锁、刷脸支付、远程身份验证等场景都会用到这一技术。但如果攻击者利用虚假视频生成技术生成逼真的换脸视频来攻击上述场景的活体检测系统,将会对这些场景的安全性产生巨大的威胁。针对这个问题使用4种先进的Deepfake技术生成大量的换脸图片和视频作为测试样本,用这些样本来对百度、腾讯等商用活体检测平台的在线API接口进行测试。测试实验结果显示常用的各大商用活体检测平台对Deepfake图像的检测成功率普遍很低,并且对图像的质量较为敏感,对真实图像的误检率也很高。其主要原因可能是这些平台设计时针对的是打印照片攻击、屏幕二次翻拍攻击、硅胶面具攻击等传统的活体检测攻击方法,并未将先进的换脸检测技术集成到他们的活体检测算法中,这些平台因此不能够有效应对Deepfake攻击。因此,提出了一种集成活体检测方法Integranet,该方法由4种针对不同图像特征的检测算法集成所得,既能够有效检测出打印照片、屏幕二次翻拍等传统的攻击手段,也能够有效应对先进的Deepfake攻击。在测试数据集上验证Integranet的检测效果,结果显示Integranet检测方法相比较各大商用活体检测平台,对Deepfake图像的检测成功率至少提高35%以上。

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.