The generalization of neural networks is harmed by shortcut learning: the use of simple non-semantic features may prevent the networks from learning deeper semantic and task-related cues. Existing studies focus mainly on explicit shortcuts, e.g. color patches and annotated text in images, that are visually detectable and may be removed. However, there exist implicit shortcuts determined by bias or superficial statistics in the data that neural networks can easily exploit. Mitigating the learning of implicit shortcuts is challenging due to the simplicity-bias and an intrinsic difficulty in identifying them. We empirically investigate shortcut learning in the frequency domain and propose a method to identify learned frequency shortcuts based on frequency removal. We found that frequency shortcuts often correspond to textures consisting of specific frequencies. We also investigate the influence of frequency shortcuts in Out-of-Distribution (OOD) tests.