Vision-Language Models vs. CNNs in Document Recognition Under Adversarial Attacks

This report synthesises findings from 16 peer-reviewed papers addressing the following research question: What is the comparative accuracy degradation of vision-language models versus standalone CNN architectures on document recognition tasks under structured adversarial attacks. 9 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.6/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the comparative accuracy degradation of vision-language models versus standalone CNN architectures on document recognition tasks under structured adversarial attacks?

Autonomous literature synthesis. Automated review score: 7.6/10. Full text and citation available at Assignee Research.