Vol. 4 No. 1 (2024): January−June 2024
Articles

Intelligent Detection and Defense Against Adversarial Content Evasion: A Multi-dimensional Feature Fusion Approach for Security Compliance

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  • Zhengyi Zhang,
  • Lichao Zhu
Zhengyi Zhang
Computer Science, Hubei University, Wuhan, China
Lichao Zhu
Master Management science and engineering, Beijing Institute of Technology, Beijing, China

Published 2024-05-07

Keywords

  • Adversarial content detection,
  • multi-dimensional feature fusion,
  • security compliance,
  • evasion countermeasures

How to Cite

Zhang, Z., & Zhu, L. (2024). Intelligent Detection and Defense Against Adversarial Content Evasion: A Multi-dimensional Feature Fusion Approach for Security Compliance. Spectrum of Research, 4(1). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/29

Copyright (c) 2024 Spectrum of Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This paper addresses the critical challenge of adversarial content evasion, where malicious actors employ sophisticated techniques to circumvent AI-driven content security systems. We propose a novel multi-dimensional feature fusion framework that integrates nine distinct feature dimensions: syntactic, semantic, stylometric, emotional, temporal, structural, contextual, visual, and behavioral. The framework employs adaptive fusion strategies with dynamic weighting mechanisms to counteract evolving evasion tactics. Experimental evaluation across five diverse datasets containing over 3.4 million content instances demonstrates the framework's superior performance compared to traditional detection methodologies. The proposed approach achieves 94.3% precision and 92.7% recall, significantly outperforming unidimensional models (78.2% precision) and ensemble classifiers (86.5% precision). Robustness assessment against eight categories of advanced evasion techniques reveals particularly strong performance against character substitution (97.3% detection rate) and style transfer (92.4% detection rate) attacks, while maintaining acceptable performance against cross-modal embedding (84.5%) and structural reorganization (86.2%) techniques. The framework's dimensional redundancy creates multiple protective layers that collectively enhance detection capabilities while providing interpretable results for compliance verification. Implementation considerations address computational efficiency challenges through optimized fusion algorithms that balance detection accuracy against resource utilization in high-volume content environments.

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