  We study the robustness of image classifiers to temporal perturbations
derived from videos. As part of this study, we construct two datasets,
ImageNet-Vid-Robust and YTBB-Robust , containing a total 57,897 images grouped
into 3,139 sets of perceptually similar images. Our datasets were derived from
ImageNet-Vid and Youtube-BB respectively and thoroughly re-annotated by human
experts for image similarity. We evaluate a diverse array of classifiers
pre-trained on ImageNet and show a median classification accuracy drop of 16
and 10 on our two datasets. Additionally, we evaluate three detection models
and show that natural perturbations induce both classification as well as
localization errors, leading to a median drop in detection mAP of 14 points.
Our analysis demonstrates that perturbations occurring naturally in videos pose
a substantial and realistic challenge to deploying convolutional neural
networks in environments that require both reliable and low-latency predictions
