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
Abstract
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.
Topics & Concepts
Robustness (evolution)Computer scienceDeep learningArtificial intelligenceDeep neural networksAdversarial systemMachine learningRanking (information retrieval)Artificial neural networkChemistryGeneBiochemistryAdversarial Robustness in Machine LearningDomain Adaptation and Few-Shot LearningAdvanced Neural Network Applications