  This paper reports the first successful application of a differentiable
architecture search (DARTS) approach to the deepfake and spoofing detection
problems. An example of neural architecture search, DARTS operates upon a
continuous, differentiable search space which enables both the architecture and
parameters to be optimised via gradient descent. Solutions based on
partially-connected DARTS use random channel masking in the search space to
reduce GPU time and automatically learn and optimise complex neural
architectures composed of convolutional operations and residual blocks. Despite
being learned quickly with little human effort, the resulting networks are
competitive with the best performing systems reported in the literature. Some
are also far less complex, containing 85% fewer parameters than a Res2Net
competitor.

Machine Learning;Sound;Audio and Speech Processing