  Neural ranking models (NRMs) have attracted considerable attention in
information retrieval. Unfortunately, NRMs may inherit the adversarial
vulnerabilities of general neural networks, which might be leveraged by
black-hat search engine optimization practitioners. Recently, adversarial
attacks against NRMs have been explored in the paired attack setting,
generating an adversarial perturbation to a target document for a specific
query. In this paper, we focus on a more general type of perturbation and
introduce the topic-oriented adversarial ranking attack task against NRMs,
which aims to find an imperceptible perturbation that can promote a target
document in ranking for a group of queries with the same topic. We define both
static and dynamic settings for the task and focus on decision-based black-box
attacks. We propose a novel framework to improve topic-oriented attack
performance based on a surrogate ranking model. The attack problem is
formalized as a Markov decision process (MDP) and addressed using reinforcement
learning. Specifically, a topic-oriented reward function guides the policy to
find a successful adversarial example that can be promoted in rankings to as
many queries as possible in a group. Experimental results demonstrate that the
proposed framework can significantly outperform existing attack strategies, and
we conclude by re-iterating that there exist potential risks for applying NRMs
in the real world.

Information Retrieval