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

SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection