Images can be censored by masking the region(s) of interest with a solid color or pattern. When a censored image is used for classification or matching, the mask itself may impact the results. Recent work in image inpainting and data augmentation provide two different approaches for dealing with censored images. In this work, we perform an extensive evaluation of these methods to understand if the impact of censoring can be mitigated for image classification and retrieval. Results indicate that modern learning-based inpainting approaches outperform augmentation strategies and that metrics typically used to evaluate inpainting performance (e.g., reconstruction accuracy) do not necessarily correspond to improved classification or retrieval, especially in the case of person-shaped masked regions.