Towards Efficient Data-Centric Robust Machine Learning with Noise-based Augmentation
This work addresses robustness for machine learning models in security-sensitive applications, but it appears incremental as it builds on existing noise and perturbation techniques.
The paper tackles the problem of improving model robustness against malicious inputs in black-box settings by proposing a noise-based data augmentation method combining Gaussian, Salt-and-Pepper, and PGD adversarial perturbations, achieving good efficiency in computation cost and robustness enhancement as shown in evaluations.
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.