BeCAPTCHA: Bot Detection in Smartphone Interaction using Touchscreen Biometrics and Mobile Sensors
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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 behaviour when interacting with the technology and improve bot detection algorithms. For this, we propose a CAPTCHA method based on the analysis of the information obtained during a single drag and drop task in combination with the accelerometer data. 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 behaviour and develop a new generation of CAPTCHAs. The experiments are evaluated with HuMIdb (Human Mobile Interaction database), a novel multimodal mobile database collected for this work that comprises 14 mobile sensors acquired from 600 users. HuMIdb is freely available to the research community.
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