Description
Audio Speech Recognition (ASR) systems are ubiquitous presences in our
online devices and their vulnerability to attacks has become a matter of
research interest. While the core machine learning algorithms that
enable these systems have already been analyzed in detail, there exists
no comparative analysis of vulnerabilities between identical machine
learning architectures trained on different language datasets. This
project investigates how ASR models for English and German hold up under
a set of attacks and whether one of the languages is more susceptible to
manipulations than the other. The results of this experiment suggest
statistically relevant differences between English and German in terms
of computational effort necessary for the successful generation of
adversarial examples.
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Thesis topic in co-operation with the Fraunhofer Institute for Applied and Integrated Security AISEC, Garching