论文标题
becaptcha：使用触摸屏和移动传感器在HumidB上测试的行为机器人检测
BeCAPTCHA: Behavioral Bot Detection using Touchscreen and Mobile Sensors benchmarked on HuMIdb
论文作者
论文摘要
在本文中，我们研究了基于智能手机互动的新一代验证码方法的适用性。与智能手机交互期间产生的数据流的异质流可以用于建模与技术交互并改善机器人检测算法时的人类行为。为此，我们提出了BeCaptcha，这是一种基于对单个拖放任务中在单个拖放任务中获得的触摸屏信息的分析，并结合加速度计数据的分析。 Becaptcha的目的是确定拖放任务是由人还是机器人实现的。我们通过生成与生成对抗神经网络和手工制作方法合成的假样品来评估该方法。我们的结果表明，移动传感器具有表征人类行为并发展新一代验证码的潜力。使用HumidB（人类移动交互数据库）评估实验，这是一个新型的多模式移动数据库，其中包括从600个用户那里获得的14个移动传感器。 HUMIDB可自由使用研究界。
In this paper we study the suitability of a new generation of CAPTCHA methods based on smartphone interactions. The heterogeneous flow of data generated during the interaction with the smartphones can be used to model human behavior when interacting with the technology and improve bot detection algorithms. For this, we propose BeCAPTCHA, a CAPTCHA method based on the analysis of the touchscreen information obtained during a single drag and drop task in combination with the accelerometer data. The goal of BeCAPTCHA is to determine whether the drag and drop task was realized by a human or a bot. We evaluate the method by generating fake samples synthesized with Generative Adversarial Neural Networks and handcrafted methods. Our results suggest the potential of mobile sensors to characterize the human behavior and develop a new generation of CAPTCHAs. The experiments are evaluated with HuMIdb (Human Mobile Interaction database), a novel multimodal mobile database that comprises 14 mobile sensors acquired from 600 users. HuMIdb is freely available to the research community.
