We propose an enhanced version of the Authentication with Built-in Camera (ABC) protocol by employing a deep learning solution based on built-in motion sensors. The standard ABC protocol identifies mobile devices based on the photo-response non-uniformity (PRNU) of the camera sensor, while also considering QR-code-based meta-information. During authentication, the user is required to take two photos that contain two QR codes presented on a screen. The presented QR code images also contain a unique probe signal, similar to a camera fingerprint, generated by the protocol. During verification, the server computes the fingerprint of the received photos and authenticates the user if (i) the probe signal is present, (ii) the metadata embedded in the QR codes is correct and (iii) the camera fingerprint is identified correctly. However, the protocol is vulnerable to forgery attacks when the attacker can compute the camera fingerprint from external photos, as shown in our preliminary work. In this context, we propose an enhancement for the ABC protocol based on motion sensor data, as an additional and passive authentication layer. Smartphones can be identified through their motion sensor data, which, unlike photos, is never posted by users on social media platforms, thus being more secure than using photographs alone. To this end, we transform motion signals into embedding vectors produced by deep neural networks, applying Support Vector Machines for the smartphone identification task. Our change to the ABC protocol results in a multi-modal protocol that lowers the false acceptance rate for the attack proposed in our previous work to a percentage as low as 0.07%.

中文翻译：

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使用内置相机协议改进身份验证使用内置运动传感器：深度学习解决方案

我们通过采用基于内置运动传感器的深度学习解决方案，提出了内置摄像头认证 (ABC) 协议的增强版本。标准 ABC 协议基于相机传感器的光响应非均匀性 (PRNU) 识别移动设备，同时还考虑了基于二维码的元信息。在认证过程中，用户需要拍摄两张包含两个二维码的照片显示在屏幕上。所呈现的二维码图像还包含一个独特的探测信号，类似于相机指纹，由协议生成。在验证过程中，服务器计算接收到的照片的指纹并在（i）存在探测信号，（ii）嵌入二维码中的元数据正确以及（iii）正确识别相机指纹时对用户进行身份验证。然而，当攻击者可以从外部照片计算相机指纹时，该协议容易受到伪造攻击，如我们的初步工作所示。在这种情况下，我们提出了基于运动传感器数据的 ABC 协议的增强，作为附加的被动身份验证层。智能手机可以通过其运动传感器数据进行识别，与照片不同，用户永远不会将其发布在社交媒体平台上，因此比单独使用照片更安全。为此，我们将运动信号转换为由深度神经网络产生的嵌入向量，将支持向量机应用于智能手机识别任务。我们对 ABC 协议的更改产生了一个多模式协议，它将我们之前工作中提出的攻击的错误接受率降低到低至 0.07% 的百分比。