Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection

TL;DR

This paper introduces a novel application of differentiable architecture search (DARTS) to deepfake and spoofing detection, resulting in efficient, high-performing neural networks with fewer parameters, reducing computational costs.

Contribution

It is the first to apply partially-connected DARTS with channel masking to deepfake detection, achieving competitive accuracy with simpler models and less human effort.

Findings

Networks are competitive with state-of-the-art systems.

Models contain 85% fewer parameters than some competitors.

Search process is fast and requires minimal human intervention.

Abstract

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.