An Empirical Study on the Effect of Training Data Perturbations on Neural Network Robustness

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 15; p. 4874
• Main Authors: Wang, Jie, Wu, Zili, Lu, Minyan, Ai, Jun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.07.2024
• Subjects: adversarial training; Algorithms; Artificial intelligence; Computational linguistics; convolutional neural network; Datasets; empirical study; Language processing; Natural language interfaces; Neural networks; Noise; perturbation; Regularization methods; robustness; Semantics; robustness; empirical study; adversarial training; convolutional neural network; perturbation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24154874

The vulnerability of modern neural networks to random noise and deliberate attacks has raised concerns about their robustness, particularly as they are increasingly utilized in safety- and security-critical applications. Although recent research efforts were made to enhance robustness through retraining with adversarial examples or employing data augmentation techniques, a comprehensive investigation into the effects of training data perturbations on model robustness remains lacking. This paper presents the first extensive empirical study investigating the influence of data perturbations during model retraining. The experimental analysis focuses on both random and adversarial robustness, following established practices in the field of robustness analysis. Various types of perturbations in different aspects of the dataset are explored, including input, label, and sampling distribution. Single-factor and multi-factor experiments are conducted to assess individual perturbations and their combinations. The findings provide insights into constructing high-quality training datasets for optimizing robustness and recommend the appropriate degree of training set perturbations that balance robustness and correctness, and contribute to understanding model robustness in deep learning and offer practical guidance for enhancing model performance through perturbed retraining, promoting the development of more reliable and trustworthy deep learning systems for safety-critical applications.