论文标题
becaptcha：使用多个内置传感器检测智能手机互动中的人类行为
BeCAPTCHA: Detecting Human Behavior in Smartphone Interaction using Multiple Inbuilt Sensors
论文作者
论文摘要
我们介绍了一个名为HUMIDB（人类移动交互数据库）的新颖的多模式移动数据库，该数据库包括从600个用户那里获得的14个移动传感器。在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互动时的人类行为。基于此新数据集，我们探讨了智能手机传感器改善机器人检测的能力。我们根据对单个拖放任务中获得的信息的分析提出了验证码方法。我们评估了生成与生成对抗神经网络和手工制作方法合成的假样品的方法。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。
We introduce a novel multimodal mobile database called HuMIdb (Human Mobile Interaction database) that comprises 14 mobile sensors acquired from 600 users. The heterogeneous flow of data generated during the interaction with the smartphones can be used to model human behavior when interacting with the technology. Based on this new dataset, we explore the capacity of smartphone sensors to improve bot detection. We propose a CAPTCHA method based on the analysis of the information obtained during a single drag and drop task. We evaluate the method 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.
