Black-box Adversarial Attacks against Dense Retrieval Models: A
  Multi-view Contrastive Learning Method
Yu-An Liu Ruqing Zhang Jiafeng Guo Maarten de Rijke Wei Chen Yixing Fan Xueqi Cheng
Neural ranking models (NRMs) and dense retrieval (DR) models have given rise
to substantial improvements in overall retrieval performance. In addition to
their effectiveness, and motivated by the proven lack of robustness of deep
learning-based approaches in other areas, there is growing interest in the
robustness of deep learning-based approaches to the core retrieval problem.
Adversarial attack methods that have so far been developed mainly focus on
attacking NRMs, with very little attention being paid to the robustness of DR
models. In this paper, we introduce the adversarial retrieval attack (AREA)
task. The AREA task is meant to trick DR models into retrieving a target
document that is outside the initial set of candidate documents retrieved by
the DR model in response to a query. We consider the decision-based black-box
adversarial setting, which is realistic in real-world search engines. To
address the AREA task, we first employ existing adversarial attack methods
designed for NRMs. We find that the promising results that have previously been
reported on attacking NRMs, do not generalize to DR models: these methods
underperform a simple term spamming method. We attribute the observed lack of
generalizability to the interaction-focused architecture of NRMs, which
emphasizes fine-grained relevance matching. DR models follow a different
representation-focused architecture that prioritizes coarse-grained
representations. We propose to formalize attacks on DR models as a contrastive
learning problem in a multi-view representation space. The core idea is to
encourage the consistency between each view representation of the target
document and its corresponding viewer via view-wise supervision signals.
Experimental results demonstrate that the proposed method can significantly
outperform existing attack strategies in misleading the DR model with small
indiscernible text perturbations.