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We propose a novel Pseudo Relevance-based ADversarial ranking Attack .We make the first at-tempt to address the WSRA task under the decision-based black-box . We propose a novel Pseudo Relevance-based ADversarial ranking Attack method (PRADA) that learns a surrogate model based on Pseudo Relevance Feedback (PRF) to .We make the first at-tempt to address the WSRA task under the decision-based black-box attack setting. Specifically, we introduce a novel Pseudo-Relevance based ADversarial ranking .
We focused on the practical decision-based black-box attack setting and developed a novel method PRADA based on the PRF idea to generate the adversarial . We propose a novel Pseudo Relevance-based ADversarial ranking Attack method (PRADA) that learns a surrogate model based on Pseudo Relevance Feedback (PRF) to .
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PRADA: Prac-tical Black-box Adversarial Attacks against Neural Ranking Models. ACM Trans. Inf. Syst. 41, 4, Article 89 (April 2023), 27 pages. https://doi.org/10.1145/3576923. 1 .We propose a novel Pseudo Relevance-based ADversarial ranking Attack method (PRADA) that learns a surrogate model based on Pseudo Relevance Feedback (PRF) to generate gradients .PRADA. Repo for our paper, PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Models, by Chen Wu, Ruqing Zhang, Jiafeng Guo, Maarten De Rijke, Yixing Fan, and . This study proposes an innovative gradient-based attack method, empowered by the pairwise objective function, to generate adversarial triggers, which causes premeditated .
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 .We focus on the decision-based black-box attack setting, where the attackers cannot directly get access to the model information, but can only query the target model to obtain the rank .
We propose a novel Pseudo Relevance-based ADversarial ranking Attack method (PRADA) that learns a surrogate model based on Pseudo Relevance Feedback (PRF) to .We make the first at-tempt to address the WSRA task under the decision-based black-box attack setting. Specifically, we introduce a novel Pseudo-Relevance based ADversarial ranking . We focused on the practical decision-based black-box attack setting and developed a novel method PRADA based on the PRF idea to generate the adversarial . We propose a novel Pseudo Relevance-based ADversarial ranking Attack method (PRADA) that learns a surrogate model based on Pseudo Relevance Feedback (PRF) to .
PRADA: Prac-tical Black-box Adversarial Attacks against Neural Ranking Models. ACM Trans. Inf. Syst. 41, 4, Article 89 (April 2023), 27 pages. https://doi.org/10.1145/3576923. 1 .
We propose a novel Pseudo Relevance-based ADversarial ranking Attack method (PRADA) that learns a surrogate model based on Pseudo Relevance Feedback (PRF) to generate gradients .
PRADA. Repo for our paper, PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Models, by Chen Wu, Ruqing Zhang, Jiafeng Guo, Maarten De Rijke, Yixing Fan, and . This study proposes an innovative gradient-based attack method, empowered by the pairwise objective function, to generate adversarial triggers, which causes premeditated . 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 .
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