Human or machine? It is not what you write, but how you write it

Luis A. Leiva, Moises Diaz, Miguel A. Ferrer, Réjean Plamondon

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Online fraud often involves identity theft. Since most security measures are weak or can be spoofed, we investigate a more nuanced and less explored avenue: behavioral biometrics via handwriting movements. This kind of data can be used to verify whether a user is operating a device or a computer application, so it is important to distinguish between human and machine-generated movements reliably. For this purpose, we study handwritten symbols (isolated characters, digits, gestures, and signatures) produced by humans and machines, and compare and contrast several deep learning models. We find that if symbols are presented as static images, they can fool state-of-the-art classifiers (near 75% accuracy in the best case) but can be distinguished with remarkable accuracy if they are presented as temporal sequences (95% accuracy in the average case). We conclude that an accurate detection of fake movements has more to do with how users write, rather than what they write. Our work has implications for computerized systems that need to authenticate or verify legitimate human users, and provides an additional layer of security to keep attackers at bay.

Original languageEnglish
Title of host publicationProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
Number of pages8
ISBN (Electronic)9781728188089
Publication statusPublished - 2021
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Pattern Recognition - Virtual, Online, Milan, Italy
Duration: 10 Jan 202115 Jan 2021
Conference number: 25

Publication series

NameInternational Conference on Pattern Recognition
ISSN (Print)1051-4651


ConferenceInternational Conference on Pattern Recognition
Abbreviated titleICPR


  • Biometrics
  • Classification
  • Deep learning
  • Handwriting
  • Kinematic models
  • Liveness detection
  • Verification


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