  Dropout is a common operator in deep learning, aiming to prevent overfitting
by randomly dropping neurons during training. This paper introduces a new
family of poisoning attacks against neural networks named DROPOUTATTACK.
DROPOUTATTACK attacks the dropout operator by manipulating the selection of
neurons to drop instead of selecting them uniformly at random. We design,
implement, and evaluate four DROPOUTATTACK variants that cover a broad range of
scenarios. These attacks can slow or stop training, destroy prediction accuracy
of target classes, and sabotage either precision or recall of a target class.
In our experiments of training a VGG-16 model on CIFAR-100, our attack can
reduce the precision of the victim class by 34.6% (from 81.7% to 47.1%) without
incurring any degradation in model accuracy
