Abstract: Invariance to diverse types of image corruption, such as noise, blurring, or colour shifts, is essential to establish robust models in computer vision. Data augmentation has been proven to improve robustness against common corruptions. However, existing augmentation strategies for boosting corruption robustness produce samples that deviate significantly from the underlying data manifold. As a result, performance is skewed toward certain types of corruption. To address this issue, we propose a multi-source vicinal transfer augmentation (VITA) method for generating diverse on-manifold samples. The proposed VITA consists of two complementary parts: tangent transfer and integration of multi-source vicinal samples. The tangent transfer creates initial augmented samples for improving corruption robustness. The integration employs a generative model to characterize the underlying manifold built by vicinal samples, facilitating in the generation of on-manifold samples. Our proposed VITA outperforms other state-of-the-art augmentation methods significantly, as demonstrated by extensive experiments on corruption benchmarks.