Robustness Analysis of Machine Learning Models Using Domain-Specific Test Data Perturbation

This study examines how perturbations in image, audio, and text inputs affect the performance of different classification models. Various perturbators were applied to three seed datasets at different intensities to produce noisy test data. Then, the models' performance was evaluated on the generated test data. Our findings indicate that there is a consistent relationship between larger perturbations and lower model performance across perturbators, models, and domains. However, this relationship varies depending on the characteristics of the specific model, dataset, and perturbator.