F$^2$AT: Feature-Focusing Adversarial Training via Disentanglement of
  Natural and Perturbed Patterns
Yaguan Qian Chenyu Zhao Zhaoquan Gu Bin Wang Shouling Ji Wei Wang Boyang Zhou Pan Zhou
Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by
well-designed perturbations. This could lead to disastrous results on critical
applications such as self-driving cars, surveillance security, and medical
diagnosis. At present, adversarial training is one of the most effective
defenses against adversarial examples. However, traditional adversarial
training makes it difficult to achieve a good trade-off between clean accuracy
and robustness since spurious features are still learned by DNNs. The intrinsic
reason is that traditional adversarial training makes it difficult to fully
learn core features from adversarial examples when adversarial noise and clean
examples cannot be disentangled. In this paper, we disentangle the adversarial
examples into natural and perturbed patterns by bit-plane slicing. We assume
the higher bit-planes represent natural patterns and the lower bit-planes
represent perturbed patterns, respectively. We propose a Feature-Focusing
Adversarial Training (F$^2$AT), which differs from previous work in that it
enforces the model to focus on the core features from natural patterns and
reduce the impact of spurious features from perturbed patterns. The
experimental results demonstrated that F$^2$AT outperforms state-of-the-art
methods in clean accuracy and adversarial robustness.