How smart your smartphone is in lie detection?
How smart your smartphone is in lie detection?
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DOI：
10.1145/3360774.3360788
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Md. Mizanur Rahman;Atanu Shome;Sriram Chellappan;A. Alim;Al Islam
中科院分区：
文献类型：
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作者：
Md. Mizanur Rahman;Atanu Shome;Sriram Chellappan;A. Alim;Al Islam
在我们与他人的日常互动中，说谎是（实际上）不可避免的组成部分，它包括口头和文字交流（例如，通过智能手机输入文本）。检测一个人是否在撒谎有着重要的应用，尤其是在智能手机无处不在的情况下，再加上如今错误信息（故意）传播的猖獗。在本文中，我们设计了一种技术来检测一个人通过智能手机输入的文本是否表明他在撒谎。为此，首先，我们明智地开发了一项基于智能手机的调查，保证任何参与者都能提供真实和虚假的回答。当参与者用短信回答每个问题时，智能手机会测量其内置惯性传感器的读数，然后计算出它所经历的震动、加速度、倾斜角度、打字速度等特征。随后，对于每个参与者（总共47人），我们通过与他们的亲身经历以及与每个参与者的非正式讨论来收集正确和错误的回答。通过比较每个参与者的反应，以及智能手机计算的相应运动特征，我们实现了几种机器学习算法来检测参与者何时撒谎，在最严格的留一评估策略中，我们的准确率约为70%。随后，利用我们的分析结果，我们开发了一个使用智能手机进行实时测谎的架构。然而，另一个用户对我们测谎系统的评价是，在检测虚假反应方面，准确率达到84%-90%。
Lying is a (practically) unavoidable component of our day to day interactions with other people, and it includes both oral and textual communications (e.g. text entered via smartphones). Detecting when a person is lying has important applications, especially with the ubiquity of messaging via smart-phones, coupled with rampant increases in (intentional) spread of mis-information today. In this paper, we design a technique to detect whether or not a person's textual inputs when typed via a smartphone indicate lying. To do so, first, we judiciously develop a smartphone based survey that guarantees any participant to provide a mix of true and false responses. While the participant is texting out responses to each question, the smartphone measures readings from its inbuilt inertial sensors, and then computes features like shaking, acceleration, tilt angle, typing speed etc. experienced by it. Subsequently, for each participant (47 in total), we glean the true and false responses using our own experiences with them, and also via informal discussions with each participant. By comparing the responses of each participant, along with the corresponding motion features computed by the smartphone, we implement several machine learning algorithms to detect when a participant is lying, and our accuracy is around 70% in the most stringent leave-one-out evaluation strategy. Later, utilizing findings of our analysis, we develop an architecture for real-time lie detection using smartphones. Yet another user evaluation of our lie detection system yields 84%-90% accuracy in detecting false responses.