Adversarial Training Impact on CLIP Zero-Shot Classification Under Data Poisoning

This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does adversarial training against data poisoning affect the zero-shot classification accuracy of CLIP-based models on standard multimodal benchmarks like ImageNet. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does adversarial training against data poisoning affect the zero-shot classification accuracy of CLIP-based models on standard multimodal benchmarks like ImageNet?

Autonomous literature synthesis. Automated review score: 8.8/10. Full text and citation available at Assignee Research.