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Adversarial examples, generated by applying small perturbations to inputfeatures, are widely used to fool classifiers and measure their robustness tonoisy inputs. However, little work has been done to evaluate the robustness ofranking models through adversarial examples. In this work, we present asystematic approach of leveraging adversarial examples to measure therobustness of popular ranking models. We explore a simple method to generateadversarial examples that forces a ranker to incorrectly rank the documents.Using this approach, we analyze the robustness of various ranking models andthe quality of perturbations generated by the adversarial attacker across twodatasets. Our findings suggest that with very few token changes (1-3), theattacker can yield semantically similar perturbed documents that can fooldifferent rankers into changing a document's score, lowering its rank byseveral positions.

One word at a time: adversarial attacks on retrieval models