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Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors

arXiv cs.LG · 2026-08-04 缓存

This paper introduces a white-box, gradient-regularized evasion framework that embeds attack logic directly into model parameters, successfully fooling explainable AI auditors like LIME, SHAP, and Integrated Gradients while bypassing anomaly detection defenses.

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