  In recent years, there has been an explosion of research into developing more
robust deep neural networks against adversarial examples. Adversarial training
appears as one of the most successful methods. To deal with both the robustness
against adversarial examples and the accuracy over clean examples, many works
develop enhanced adversarial training methods to achieve various trade-offs
between them. Leveraging over the studies that smoothed update on weights
during training may help find flat minima and improve generalization, we
suggest reconciling the robustness-accuracy trade-off from another perspective,
i.e., by adding random noise into deterministic weights. The randomized weights
enable our design of a novel adversarial training method via Taylor expansion
of a small Gaussian noise, and we show that the new adversarial training method
can flatten loss landscape and find flat minima. With PGD, CW, and Auto
Attacks, an extensive set of experiments demonstrate that our method enhances
the state-of-the-art adversarial training methods, boosting both robustness and
clean accuracy. The code is available at
https://github.com/Alexkael/Randomized-Adversarial-Training.
