EchoPrint: Two-factor Authentication using Acoustics and Vision on Smartphones
EchoPrint: Two-factor Authentication using Acoustics and Vision on Smartphones
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DOI：
10.1145/3241539.3241575
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发表时间：
2018-10
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影响因子：
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通讯作者：
Bing Zhou;J. Lohokare;Ruipeng Gao;Fan Ye
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文献类型：
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作者：
Bing Zhou;J. Lohokare;Ruipeng Gao;Fan Ye
智能手机上的用户身份验证必须同时满足安全性和便利性，这是一种天生难以平衡的艺术。苹果的FaceID可以说是最新的此类努力，代价是额外的硬件（例如，点投影仪、泛光照明器和红外照相机）。我们提出了一种新型用户身份验证系统EchoPrint，它利用声学和视觉来实现安全、便捷的用户身份验证，无需任何特殊硬件。EchoPrint从耳机扬声器主动发出几乎听不见的声学信号，以“照亮”用户的面部，并通过从3D面部轮廓反弹的回声中提取的独特特征来认证用户。为了应对手机持有姿势的变化，因此回声，卷积神经网络（CNN）被训练来提取可靠的声学特征，这些特征进一步与视觉面部地标位置相结合，以提供二进制支持向量机（SVM）分类器进行最终认证。由于回波特征取决于3D面部几何形状，因此EchoPrint不像2D视觉面部识别系统那样容易被图像或视频欺骗。它只需要商品硬件，从而避免了FaceID等解决方案中特殊传感器的额外成本。对62名志愿者和非人类物体（如图像、照片和雕塑）的实验表明，EchoPrint实现了93.75%的平衡准确率和93.50%的F分数，而平均准确率为98.05%，并且没有观察到基于图像/视频的攻击成功欺骗。
User authentication on smartphones must satisfy both security and convenience, an inherently difficult balancing art. Apple's FaceID is arguably the latest of such efforts, at the cost of additional hardware (e.g., dot projector, flood illuminator and infrared camera). We propose a novel user authentication system EchoPrint, which leverages acoustics and vision for secure and convenient user authentication, without requiring any special hardware. EchoPrint actively emits almost inaudible acoustic signals from the earpiece speaker to "illuminate" the user's face and authenticates the user by the unique features extracted from the echoes bouncing off the 3D facial contour. To combat changes in phone-holding poses thus echoes, a Convolutional Neural Network (CNN) is trained to extract reliable acoustic features, which are further combined with visual facial landmark locations to feed a binary Support Vector Machine (SVM) classifier for final authentication. Because the echo features depend on 3D facial geometries, EchoPrint is not easily spoofed by images or videos like 2D visual face recognition systems. It needs only commodity hardware, thus avoiding the extra costs of special sensors in solutions like FaceID. Experiments with 62 volunteers and non-human objects such as images, photos, and sculptures show that EchoPrint achieves 93.75% balanced accuracy and 93.50% F-score, while the average precision is 98.05%, and no image/video based attack is observed to succeed in spoofing.