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Spanish Patent Application (P5321ES00), expandable to international protection, 100% Autonoma University of Madrid (UAM).


  • Licensing agreement.
  • R&D development agreement.

For further information we recommend:


A. Acien, A. Morales, J. Fierrez, R. Vera-Rodriguez, I. Bartolome, “Be-CAPTCHA: Detecting Human Behavior in Smartphone Interaction using Multiple Inbuilt Sensors,” Proc. of AAAI Workshop on Artificial for Cyber Security (AICS), New York, USA, 2020. [pdf]

A. Acien, A. Morales, J. Fierrez, R. Vera-Rodriguez, “BeCAPTCHA-Mouse: Synthetic Mouse Trajectories and Improved Bot Detection,” arXiv:2005.00890, 2020. [pdf][GitHub]

A. Acien, A. Morales, J. Fierrez, R. Vera-Rodriguez, O. Delgado-Mohatar, “BeCAPTCHA: Bot Detection in Smartphone Interaction using Touchscreen Biometrics and Mobile Sensors“, arXiv:2005.13655, 2020. [pdf][Github]

HuMIdb GitHub:

We present a new experimental framework aimed to study how multimodal machine learning is influenced by biases pHuMIdb dataset, a novel multimodal mobile database that comprises more than 5 GB from a wide range of mobile sensors acquired under unsupervised scenario. The dataset includes 14 sensors (see Table 1 for the details) during natural human-mobile interaction performed by more than 600 users. For the acquisition, we implemented an Android application that collects the sensor signals while the users complete 8 simple tasks with their own smartphones and without any supervision whatsoever (i.e., the users could be standing, sitting, walking, indoors, outdoors, at daytime or night, etc.). All data captured in this database have been stored in private servers and anonymized with previous participant consent according to the GDPR (General Data Protection Regulation).

Visit BeCAPTCHA-HuMIdb GitHub

BeCAPTCHA-Mouse GitHub:

BeCAPTCHA-Mouse Benchmark for development of bot detection technologyes based on mouse dynamics. BeCAPTCHA-Mouse benchmark contains more than 10K synthetic mouse trayectories generated with two methods.

Visit BeCAPTCHA-Mouse GitHub

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