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Deep learning models suffer from a phenomenon called adversarial attacks: wecan apply minor changes to the model input to fool a classifier for aparticular example. The literature mostly considers adversarial attacks onmodels with images and other structured inputs. However, the adversarialattacks for categorical sequences can also be harmful. Successful attacks forinputs in the form of categorical sequences should address the followingchallenges: (1) non-differentiability of the target function, (2) constraintson transformations of initial sequences, and (3) diversity of possibleproblems. We handle these challenges using two black-box adversarial attacks.The first approach adopts a Monte-Carlo method and allows usage in anyscenario, the second approach uses a continuous relaxation of models and targetmetrics, and thus allows usage of state-of-the-art methods for adversarialattacks with little additional effort. Results for money transactions, medicalfraud, and NLP datasets suggest that proposed methods generate reasonableadversarial sequences that are close to original ones but fool machine learningmodels.

Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world