  With the thriving of deep learning and the widespread practice of using
pre-trained networks, backdoor attacks have become an increasing security
threat drawing many research interests in recent years. A third-party model can
be poisoned in training to work well in normal conditions but behave
maliciously when a trigger pattern appears. However, the existing backdoor
attacks are all built on noise perturbation triggers, making them noticeable to
humans. In this paper, we instead propose using warping-based triggers. The
proposed backdoor outperforms the previous methods in a human inspection test
by a wide margin, proving its stealthiness. To make such models undetectable by
machine defenders, we propose a novel training mode, called the ``noise mode.
The trained networks successfully attack and bypass the state-of-the-art
defense methods on standard classification datasets, including MNIST, CIFAR-10,
GTSRB, and CelebA. Behavior analyses show that our backdoors are transparent to
network inspection, further proving this novel attack mechanism's efficiency.
