  Clean-label (CL) attack is a form of data poisoning attack where an adversary
modifies only the textual input of the training data, without requiring access
to the labeling function. CL attacks are relatively unexplored in NLP, as
compared to label flipping (LF) attacks, where the latter additionally requires
access to the labeling function as well. While CL attacks are more resilient to
data sanitization and manual relabeling methods than LF attacks, they often
demand as high as ten times the poisoning budget than LF attacks. In this work,
we first introduce an Adversarial Clean Label attack which can adversarially
perturb in-class training examples for poisoning the training set. We then show
that an adversary can significantly bring down the data requirements for a CL
attack, using the aforementioned approach, to as low as 20% of the data
otherwise required. We then systematically benchmark and analyze a number of
defense methods, for both LF and CL attacks, some previously employed solely
for LF attacks in the textual domain and others adapted from computer vision.
We find that text-specific defenses greatly vary in their effectiveness
depending on their properties.
