  The data-centric machine learning aims to find effective ways to build
appropriate datasets which can improve the performance of AI models. In this
paper, we mainly focus on designing an efficient data-centric scheme to improve
robustness for models towards unforeseen malicious inputs in the black-box test
settings. Specifically, we introduce a noised-based data augmentation method
which is composed of Gaussian Noise, Salt-and-Pepper noise, and the PGD
adversarial perturbations. The proposed method is built on lightweight
algorithms and proved highly effective based on comprehensive evaluations,
showing good efficiency on computation cost and robustness enhancement. In
addition, we share our insights about the data-centric robust machine learning
gained from our experiments.
