Motion artifacts can have a detrimental effect on the analysis of chest CT scans, because the artifacts can mimic or obscure genuine pathological features. Localising motion artifacts in the lungs can improve diagnosis quality. The diverse appearance of artifacts requires large quantities of annotations to train a detection model, but manual annotations can be subjective, unreliable, and are labour intensive to obtain. We propose a novel method (code is available at https:// github.com/guusvanderham/artificial-motion-artifacts-for-ct) for generating artificial motion artifacts in chest CT images, based on simulated CT reconstruction. We use these artificial artifacts to train fully convolutional networks that can detect real motion artifacts in chest CT scans. We evaluate our method on scans from the public LIDC, RIDER and COVID19-CT datasets and find that it is possible to train detection models with artificially generated artifacts. Generated artifacts greatly improve performance when the availability of manually annotated scans is limited.