  We introduce ShortcutGen, a new data poisoning attack that generates
sample-dependent, error-minimizing perturbations by learning a generator. The
key novelty of ShortcutGen is the use of a randomly-initialized discriminator,
which provides spurious shortcuts needed for generating poisons. Different from
recent, iterative methods, our ShortcutGen can generate perturbations with only
one forward pass in a label-free manner, and compared to the only existing
generative method, DeepConfuse, our ShortcutGen is faster and simpler to train
while remaining competitive. We also demonstrate that integrating a simple
augmentation strategy can further boost the robustness of ShortcutGen against
early stopping, and combining augmentation and non-augmentation leads to new
state-of-the-art results in terms of final validation accuracy, especially in
the challenging, transfer scenario. Lastly, we speculate, through uncovering
its working mechanism, that learning a more general representation space could
allow ShortcutGen to work for unseen data.
