One-to-Many Defense Strategy Effects on Zero-Shot Image-Text Retrieval Under Multimodal Adversarial Attacks

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: How does the one-to-many relationship defense strategy impact zero-shot image-text retrieval accuracy on MS-COCO under simultaneous multimodal adversarial perturbations compared to standard. Pre-trained vision-language (VL) models are highly vulnerable to adversarial attacks. However, existing defense methods primarily focus on image classification, overlooking two key aspects of VL tasks: multimodal attacks, where both image and text can be perturbed, and the. 13 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the one-to-many relationship defense strategy impact zero-shot image-text retrieval accuracy on MS-COCO under simultaneous multimodal adversarial perturbations compared to standard adversarial training?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance