Scaling One-to-Many Defense Strategies in Multimodal Models Without Retrieval Performance Loss

Abstract

Abstract: This report synthesises findings from 16 peer-reviewed papers addressing the following research question: Can the one-to-many relationship-based defense strategy scale effectively to larger multimodal models (e.g., CLIP, BLIP) without degrading performance on standard retrieval metrics (R@1, R@5, R@10). 10 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

Can the one-to-many relationship-based defense strategy scale effectively to larger multimodal models (e.g., CLIP, BLIP) without degrading performance on standard retrieval metrics (R@1, R@5, R@10) on the SBU and MSCOCO benchmarks?

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