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Gradient-Based Adversarial Ranking Attack

WU Chen;ZHANG Ruqing;GUO Jiafeng;FAN Yixing;Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences;School of Computer and Control Engineering, University of Chinese Academy of Sciences;  
Ranking competition is prevalent in Web retrieval, and undesirable effects are caused by this adversarial attack behavior. Thus, the study on attack methods is conducive to designing a more robust ranking model.The existing attack methods are recognized by people easily and cannot attack neural ranking models effectively.In this paper, a gradient-based adversarial attack method(GARA) is proposed, including gradient-based word importance ranking, gradient-based adversarial ranking attack and embedding-based word replacement. Given a target ranking model, the backpropagation is firstly conducted based on the constructed ranking-based adversarial attack objective. Then the most important words of a specific document is recognized based on the gradient information. These important words are perturbed in the word embedding space based on the projected gradient descent. Finally, by adopting the counter-fitting technology, the document perturbation is completed by substituting the important word with its synonym which is semantically similar to the original word and nearest to the perturbed word vector.Experiments on MQ2007 and MS MARCO datasets demonstrate the effectiveness of the proposed method.
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