LiveScreen: Video Chat Liveness Detection Leveraging Skin Reflection
LiveScreen: Video Chat Liveness Detection Leveraging Skin Reflection
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DOI：
10.1109/infocom41043.2020.9155400
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发表时间：
2020-07
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影响因子：
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通讯作者：
Hongbo Liu;Zhihua Li;Yucheng Xie;Ruizhe Jiang;Yan Wang;Xiaonan Guo;Yingying Chen
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文献类型：
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作者：
Hongbo Liu;Zhihua Li;Yucheng Xie;Ruizhe Jiang;Yan Wang;Xiaonan Guo;Yingying Chen
社交媒体和通信技术的快速发展使视频聊天成为日常交流的重要和便捷方式。然而，这样的便利性也使得个人视频片段很容易被恶意用户获取并利用，从而发动诈骗攻击。现有的研究仅针对使用伪造面具的攻击，而针对使用虚拟摄像机的回放攻击的活性检测仍然是难以捉摸的。在这项工作中，我们开发了一种新的视频聊天活性检测系统，LiveScreen，它可以跟踪微弱的光的变化，利用彩色特征空间的差异，从人脸的皮肤反射。我们设计了一个不显眼的挑战帧与最小的干预视频聊天，并开发了一个强大的异常帧检测器，以验证在视频聊天中使用的响应的挑战帧的远程用户的活性。此外，我们提出了弹性防御策略，以击败天真和智能播放攻击，利用空间和时间验证。我们在笔记本电脑和智能手机平台上实现了一个原型，并在各种现实场景中进行了广泛的实验。我们表明，我们的系统可以实现强大的活性检测，准确率和错误检测率分别为97.7%（94.8%）和1%（1.6%）的智能手机（笔记本电脑）。
The rapid advancement of social media and communication technology enables video chat to become an important and convenient way of daily communication. However, such convenience also makes personal video clips easily obtained and exploited by malicious users who launch scam attacks. Existing studies only deal with the attacks that use fabricated facial masks, while the liveness detection that targets the playback attacks using a virtual camera is still elusive. In this work, we develop a novel video chat liveness detection system, LiveScreen, which can track the weak light changes reflected off the skin of a human face leveraging chromatic eigenspace differences. We design an inconspicuous challenge frame with minimal intervention to the video chat and develop a robust anomaly frame detector to verify the liveness of the remote user in the video chat using the response to the challenge frame. Furthermore, we propose resilient defense strategies to defeat both naive and intelligent playback attacks leveraging spatial and temporal verification. We implemented a prototype over both laptop and smartphone platforms and conducted extensive experiments in various realistic scenarios. We show that our system can achieve robust liveness detection with accuracy and false detection rates 97.7% (94.8%) and 1% (1.6%) on smartphones (laptops), respectively.