Neural ranking models (NRMs) have attracted considerable attention in information retrieval.
Unfortunately, NRMs may inherit the adversarial vulnerabilities of general neural networks …

Adversarial ranking attacks have gained increasing attention due to their success in probing
vulnerabilities, and, hence, enhancing the robustness, of neural ranking models …

Neural text ranking models have witnessed significant advancement and are increasingly
being deployed in practice. Unfortunately, they also inherit adversarial vulnerabilities of …

Neural ranking models (NRMs) and dense retrieval (DR) models have given rise to
substantial improvements in overall retrieval performance. In addition to their effectiveness …

Explainable information retrieval is an emerging research area aiming to make transparent
and trustworthy information retrieval systems. Given the increasing use of complex machine …

Deep cross-modal hashing has promoted the field of multi-modal retrieval due to its
excellent efficiency and storage, but its vulnerability to backdoor attacks is rarely studied …

Recent advances in neural information retrieval (IR) models have significantly enhanced
their effectiveness over various IR tasks. The robustness of these models, essential for …

Neural ranking models (NRMs) have shown great success in information retrieval (IR). But
their predictions can easily be manipulated using adversarial examples, which are crafted …

Adversarial attacks have gained traction in order to identify potential vulnerabilities in neural
ranking models (NRMs), but current attack methods often introduce grammatical errors …

Modern sequence-to-sequence relevance models like monoT5 can effectively capture
complex textual interactions between queries and documents through cross-encoding …