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Related Experiment Video

Updated: May 28, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Symmetry-Driven Multimodal Adversarial Attacks: An Information-Theoretic Perspective on Cross-Modal Invariance and

Jin Wei1, Xinyuan Wang2, Liam Xu3

  • 1School of Computer Science and Technology, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China.

Entropy (Basel, Switzerland)
|May 26, 2026
PubMed
Summary

This study introduces a novel symmetry-driven adversarial attack for multimodal AI models like CLIP and ALBEF. The new method exploits cross-modal symmetries to create effective, imperceptible perturbations, improving model robustness.

Keywords:
adversarial attacksadversarial machine learningcross-modal synergymultimodal datasymmetry

Related Experiment Videos

Last Updated: May 28, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Natural Language Processing

Background:

  • Multimodal models (e.g., CLIP, ALBEF) align different data types by maximizing mutual information.
  • This alignment relies on semantic consistency but creates vulnerabilities due to invariant information coupling.

Purpose of the Study:

  • Investigate the structural vulnerabilities of multimodal models.
  • Propose a novel symmetry-driven adversarial attack framework to exploit these vulnerabilities.

Main Methods:

  • Developed collaborative perturbations by modeling semantic-consistent mappings between image transformations and text variations.
  • Exploited information redundancy in cross-modal symmetries.
  • Reduced the entropy of the adversarial search space.

Main Results:

  • Achieved state-of-the-art attack success rates.
  • Demonstrated effectiveness with imperceptible perturbations.
  • Revealed a trade-off between information invariance and model robustness.

Conclusions:

  • Symmetry-driven adversarial attacks offer a new way to probe and improve multimodal model robustness.
  • The findings highlight the inherent trade-offs in current multimodal alignment strategies.