Intrinsic Explanation of Random Subspace Method for Enhanced Security Applications

Yanting Wang, Jinyuan Jia

Keywords: Certified Defense, Feature Attribution

TL;DR: This paper proposes an intrinsically faithful and secure feature attribution method for random subspace method

Abstract: Random subspace method has wide security applications such as providing certified defenses against adversarial and backdoor attacks, and building robustly aligned LLM against jailbreaking attacks. However, the explanation of random subspace method lacks sufficient exploration. Existing state-of-the-art feature attribution methods such as Shapley value and LIME are computationally impractical and lacks security guarantee when applied to random subspace method. In this work, we propose EnsembleSHAP, an intrinsically faithful and secure feature attribution for random subspace method that reuses its computational byproducts. Specifically, our feature attribution method is 1) computationally efficient, 2) maintains essential properties of effective feature attribution (such as local accuracy), and 3) offers guaranteed protection against attacks on feature attribution methods. We perform comprehensive evaluations for our explanation's effectiveness when faced with different empirical attacks. Our experimental results demonstrates that our explanation not only faithfully reports the most important features, but also certifiably detects the harmful features embedded in the input sample.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 10873