  Images, captured by a camera, play a critical role in training Deep Neural
Networks (DNNs). Usually, we assume the images acquired by cameras are
consistent with the ones perceived by human eyes. However, due to the different
physical mechanisms between human-vision and computer-vision systems, the final
perceived images could be very different in some cases, for example shooting on
digital monitors. In this paper, we find a special phenomenon in digital image
processing, the moir\'e effect, that could cause unnoticed security threats to
DNNs. Based on it, we propose a Moir\'e Attack (MA) that generates the
physical-world moir\'e pattern adding to the images by mimicking the shooting
process of digital devices. Extensive experiments demonstrate that our proposed
digital Moir\'e Attack (MA) is a perfect camouflage for attackers to tamper
with DNNs with a high success rate ($100.0\%$ for untargeted and $97.0\%$ for
targeted attack with the noise budget $\epsilon=4$), high transferability rate
across different models, and high robustness under various defenses.
Furthermore, MA owns great stealthiness because the moir\'e effect is
unavoidable due to the camera's inner physical structure, which therefore
hardly attracts the awareness of humans. Our code is available at
https://github.com/Dantong88/Moire_Attack.
