Date of Submission
2025
Date of Award
6-2025
Institute Name (Publisher)
Indian Statistical Institute
Document Type
Master's Dissertation
Degree Name
Master of Technology
Subject Name
Computer Science
Department
Computer Vision and Pattern Recognition Unit (CVPR-Kolkata)
Supervisor
Majumdar, Debapriyo
Abstract (Summary of the Work)
Neural Ranking Models (NRMs) have become state-of-the-art in information retrieval, demonstrating remarkable effectiveness across various search and ranking tasks. However, their increasing deployment in real-world systems raises critical concerns about their robustness and susceptibility to adversarial attacks. This project investigates the fragility of modern NRMs by proposing and evaluating a document perturbation method based on targeted, single-word perturbation. Our approach strategically identifies an influential word depending on the query to be substituted or added in the document. We have done experiments on benchmark datasets to assess the impact of these minimal perturbations on ranking performance. Our findings reveal that even a single carefully chosen word addition or substitution can significantly change the ranking score of the targeted document providing insight into the NRMs.
Control Number
CS2333
DOI
https://dspace.isical.ac.in/items/e9652848-774b-4a45-9cde-c6d3d7299c67
DSpace Identifier
http://hdl.handle.net/10263/7567
Recommended Citation
Karmakar, Tanmay, "Word Level Attack for Text Ranking" (2025). Master’s Dissertations. 445.
https://digitalcommons.isical.ac.in/masters-dissertations/445