In this presentation the speaker will demonstrate attacks that target the data scaling process in popular deep learning examples. By carefully crafting input data that mismatches with the scales used by deep learning models, the speaker will show how an attacker can successfully evade image classification even when applications use well-trained deep learning models. The speaker will also present a few potential defending strategies to detect or mitigate such data-flow attacks.