  Visual AI systems are vulnerable to natural and synthetic physical corruption
in the real-world. Such corruption often arises unexpectedly and alters the
model's performance. In recent years, the primary focus has been on adversarial
attacks. However, natural corruptions (e.g., snow, fog, dust) are an
omnipresent threat to visual AI systems and should be considered equally
important. Many existing works propose interesting solutions to train robust
models against natural corruption. These works either leverage image
augmentations, which come with the additional cost of model training, or place
suspicious patches in the scene to design unadversarial examples. In this work,
we propose the idea of naturalistic support artifacts (NSA) for robust
prediction. The NSAs are shown to be beneficial in scenarios where model
parameters are inaccessible and adding artifacts in the scene is feasible. The
NSAs are natural looking objects generated through artifact training using
DC-GAN to have high visual fidelity in the scene. We test against natural
corruptions on the Imagenette dataset and observe the improvement in prediction
confidence score by four times. We also demonstrate NSA's capability to
increase adversarial accuracy by 8\% on average. Lastly, we qualitatively
analyze NSAs using saliency maps to understand how they help improve prediction
confidence.
