Various techniques have been proposed in different literature to analyze biometric samples collected from users. However, not much attention has been paid to the inverse problem, which consists of synthesizing artificial biometric samples that can be used for testing existing biometric systems or protecting them against forgeries. This book presents a framework for mouse dynamics biometrics synthesis. Mouse dynamics biometric is a behavioral biometric technology, which allows user recognition based on the actions received from the mouse input device while interacting with a graphical user interface. The proposed inverse biometric model learns from random raw samples collected from real users and then creates synthetic mouse actions for artificial users. The generated mouse actions have behavioral properties similar to real mouse actions but at the same time they possess their own behavior. This is shown through various comparisons of behavioral metrics as well as a Kolmogorov-Smirnov test. We also show through a 2-fold cross validation test that by submitting sample synthetic data to an existing mouse biometrics analysis model we achieve comparable performance results as when the model is applied to real mouse data.

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