  Audio DeepFakes are utterances generated with the use of deep neural
networks. They are highly misleading and pose a threat due to use in fake news,
impersonation, or extortion. In this work, we focus on increasing accessibility
to the audio DeepFake detection methods by providing SpecRNet, a neural network
architecture characterized by a quick inference time and low computational
requirements. Our benchmark shows that SpecRNet, requiring up to about 40% less
time to process an audio sample, provides performance comparable to LCNN
architecture - one of the best audio DeepFake detection models. Such a method
can not only be used by online multimedia services to verify a large bulk of
content uploaded daily but also, thanks to its low requirements, by average
citizens to evaluate materials on their devices. In addition, we provide
benchmarks in three unique settings that confirm the correctness of our model.
They reflect scenarios of low-resource datasets, detection on short utterances
and limited attacks benchmark in which we take a closer look at the influence
of particular attacks on given architectures.
