Measuring the Effect of Causal Disentanglement on the Adversarial
  Robustness of Neural Network Models
Preben M. Ness Dusica Marijan Sunanda Bose
Causal Neural Network models have shown high levels of robustness to
adversarial attacks as well as an increased capacity for generalisation tasks
such as few-shot learning and rare-context classification compared to
traditional Neural Networks. This robustness is argued to stem from the
disentanglement of causal and confounder input signals. However, no
quantitative study has yet measured the level of disentanglement achieved by
these types of causal models or assessed how this relates to their adversarial
robustness.
  Existing causal disentanglement metrics are not applicable to deterministic
models trained on real-world datasets. We, therefore, utilise metrics of
content/style disentanglement from the field of Computer Vision to measure
different aspects of the causal disentanglement for four state-of-the-art
causal Neural Network models. By re-implementing these models with a common
ResNet18 architecture we are able to fairly measure their adversarial
robustness on three standard image classification benchmarking datasets under
seven common white-box attacks. We find a strong association (r=0.820, p=0.001)
between the degree to which models decorrelate causal and confounder signals
and their adversarial robustness. Additionally, we find a moderate negative
association between the pixel-level information content of the confounder
signal and adversarial robustness (r=-0.597, p=0.040).