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Visual AI systems are vulnerable to natural and synthetic physical corruptionin the real-world. Such corruption often arises unexpectedly and alters themodel's performance. In recent years, the primary focus has been on adversarialattacks. However, natural corruptions (e.g., snow, fog, dust) are anomnipresent threat to visual AI systems and should be considered equallyimportant. Many existing works propose interesting solutions to train robustmodels against natural corruption. These works either leverage imageaugmentations, which come with the additional cost of model training, or placesuspicious patches in the scene to design unadversarial examples. In this work,we propose the idea of naturalistic support artifacts (NSA) for robustprediction. The NSAs are shown to be beneficial in scenarios where modelparameters are inaccessible and adding artifacts in the scene is feasible. TheNSAs are natural looking objects generated through artifact training usingDC-GAN to have high visual fidelity in the scene. We test against naturalcorruptions on the Imagenette dataset and observe the improvement in predictionconfidence score by four times. We also demonstrate NSA's capability toincrease adversarial accuracy by 8\% on average. Lastly, we qualitativelyanalyze NSAs using saliency maps to understand how they help improve predictionconfidence.

NSA: Naturalistic Support Artifact to Boost Network Confidence