  Systematic error, which is not determined by chance, often refers to the
inaccuracy (involving either the observation or measurement process) inherent
to a system. In this paper, we exhibit some long-neglected but
frequent-happening adversarial examples caused by systematic error. More
specifically, we find the trained neural network classifier can be fooled by
inconsistent implementations of image decoding and resize. This tiny difference
between these implementations often causes an accuracy drop from training to
deployment. To benchmark these real-world adversarial examples, we propose
ImageNet-S dataset, which enables researchers to measure a classifier's
robustness to systematic error. For example, we find a normal ResNet-50 trained
on ImageNet can have 1%-5% accuracy difference due to the systematic error.
Together our evaluation and dataset may aid future work toward real-world
robustness and practical generalization.
