BeCAPTCHA: Detecting Human Behavior in Smartphone Interaction using Multiple Inbuilt Sensors
This addresses bot detection for smartphone security, though it appears incremental as it builds on existing CAPTCHA and sensor-based approaches.
The researchers tackled the problem of bot detection by creating a multimodal mobile database (HuMIdb) with 14 sensors from 600 users and proposing a CAPTCHA method based on analyzing smartphone interaction during a drag-and-drop task, showing potential for characterizing human behavior.
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.