ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection

Wanying Ge, Michele Panariello, Jose Patino, Massimiliano Todisco, Nicholas Evans

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.


doi: 10.21437/Interspeech.2021-1187

Cite as: Ge, W., Panariello, M., Patino, J., Todisco, M., Evans, N. (2021) Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection. Proc. Interspeech 2021, 4319-4323, doi: 10.21437/Interspeech.2021-1187

@inproceedings{ge21c_interspeech,
  author={Wanying Ge and Michele Panariello and Jose Patino and Massimiliano Todisco and Nicholas Evans},
  title={{Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection}},
  year=2021,
  booktitle={Proc. Interspeech 2021},
  pages={4319--4323},
  doi={10.21437/Interspeech.2021-1187}
}