  Modern classification algorithms are susceptible to adversarial
examples--perturbations to inputs that cause the algorithm to produce
undesirable behavior. In this work, we seek to understand and extend
adversarial examples across domains in which inputs are discrete, particularly
across new domains, such as computational biology. As a step towards this goal,
we formalize a notion of synonymous adversarial examples that applies in any
discrete setting and describe a simple domain-agnostic algorithm to construct
such examples. We apply this algorithm across multiple domains--including
sentiment analysis and DNA sequence classification--and find that it
consistently uncovers adversarial examples. We seek to understand their
prevalence theoretically and we attribute their existence to spurious token
correlations, a statistical phenomenon that is specific to discrete spaces. Our
work is a step towards a domain-agnostic treatment of discrete adversarial
examples analogous to that of continuous inputs.
