An Improved Framework for Content‐ and Link‐Based Web‐Spam Detection: A Combined Approach

Shahzad, Asim; Nawi, Nazri Mohd; Rehman, Muhammad Zubair; Khan, Abdullah

doi:10.1155/2021/6625739

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…To answer RQ5, we analyze the effect of different numbers of important tokens 𝑚 for PRADA on the attack performance. Specifically, we compare PRADA with the best performing baseline TS 𝑟𝑒𝑝 on the MS-MARCO-Doc and set 𝑚 to six different values (i.e., 10,20,30,40,50,60). Note that the selected number 𝑛 of TS 𝑟𝑒𝑝 is equal to 𝑚.…”

Section: Analysis Of the Number Of Important Tokensmentioning

confidence: 99%

“…Since adversarial examples are maliciously crafted by adding perturbations that are imperceptible to humans to legitimate examples, they may not be detected by traditional anti-spamming methods [50]. Up to now, little attention has been paid to adversarial attacks against NRMs, except for analyses of the robustness of ranking models carried out by Goren et al [15].…”

Section: Introductionmentioning

confidence: 99%

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PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Models

Chen¹,

Zhang²,

Guo³

et al. 2022

Preprint

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Neural ranking models (NRMs) have shown remarkable success in recent years, especially with pre-trained language models. However, deep neural models are notorious for their vulnerability to adversarial examples. Adversarial attacks may become a new type of web spamming technique given our increased reliance on neural information retrieval models. Therefore, it is important to study potential adversarial attacks to identify vulnerabilities of NRMs before they are deployed.In this paper, we introduce the Adversarial Document Ranking Attack (ADRA) task against NRMs, which aims to promote a target document in rankings by adding adversarial perturbations to its text. We focus on the decision-based black-box attack setting, where the attackers have no access to the model parameters and gradients, but can only acquire the rank positions of the partial retrieved list by querying the target model. This attack setting is realistic in real-world search engines. We propose a novel Pseudo Relevancebased ADversarial ranking Attack method (PRADA) that learns a surrogate model based on Pseudo Relevance Feedback (PRF) to generate gradients for finding the adversarial perturbations.Experiments on two web search benchmark datasets show that PRADA can outperform existing attack strategies and successfully fool the NRM with small indiscernible perturbations of text. CCS CONCEPTS• Information systems → Retrieval models and ranking; Adversarial retrieval.

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Section: Analysis Of the Number Of Important Tokensmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Models

Chen¹,

Zhang²,

Guo³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…is method can be applied in the optimization of the structure-borne transmission path for construction machinery cab and vehicle body. e paper by Asim Shahzad et al entitled "An Improved Framework for Content-and Link-Based Web-Spam Detection: A Combined Approach" [14] aims to propose an improved framework for content-and link-based web-spam identification.…”

mentioning

confidence: 99%