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We study how networking corruptions--data corruptions caused by networkingerrors--affect video machine learning (ML) models. We discover apparentnetworking corruptions in Kinetics-400, a benchmark video ML dataset. In asimulation study, we investigate (1) what artifacts networking corruptionscause, (2) how such artifacts affect ML models, and (3) whether standardrobustness methods can mitigate their negative effects. We find that networkingcorruptions cause visual and temporal artifacts (i.e., smeared colors or framedrops). These networking corruptions degrade performance on a variety of videoML tasks, but effects vary by task and dataset, depending on how much temporalcontext the tasks require. Lastly, we evaluate data augmentation--a standarddefense for data corruptions--but find that it does not recover performance.

Lost in Transmission: On the Impact of Networking Corruptions on Video Machine Learning Models