Deep convolutional neural networks have been shown to be vulnerable to arbitrary geometric transformations. However, there is no systematic method to measure the invariance properties of deep networks to such transformations. ManiFool is a simple yet scalable algorithm to measure the invariance of deep networks. In particular, it measures the robustness of deep networks to geometric transformations in a worst-case regime as they can be problematic for sensitive applications.