Mitigating replay attacks using Darwinian-based Feature Extraction

Abstract

In [1,2], a Genetic and Evolutionary Biometric Security (GEBS) application was presented for preventing biometric replay attacks. This technique used Genetic and Evolutionary Feature Extraction - Machine Learning (GEFE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ML</sub> ) to create disposable feature extractors (FEs). These disposable FEs had higher recognition accuracy than a traditional feature extraction approach, known as the local binary pattern method. In [3], a two-stage process for developing FEs was developed. This technique is known as Darwinian Feature Extraction (DFE), and it created Darwinian FEs (dFEs) that had even higher recognition accuracy than GEFE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ML</sub> while maintaining a lower computational complexity. In this paper, we apply dFEs towards mitigating replay attacks and compare the results to disposable FEs using GEFE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ML</sub> . Our results show the effectiveness of GEFE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ML</sub> and DFE towards creating dFEs.