Increasing shape-bias in deep neural networks has been shown to improve
robustness to common corruptions and noise. In this paper we analyze the
adversarial robustness of texture and shape-biased models to Universal
Adversarial Perturbations (UAPs). We use UAPs to evaluate the robustness of DNN
models with varying degrees of shape-based training. We find that shape-biased
models do not markedly improve adversarial robustness, and we show that
ensembles of texture and shape-biased models can improve universal adversarial
robustness while maintaining strong performance.