Enhancing Adversarial Transferability via Information Bottleneck Constraints
IEEE Signal Processing Letters(2024)
摘要
From the perspective of information bottleneck (IB) theory, we propose a
novel framework for performing black-box transferable adversarial attacks named
IBTA, which leverages advancements in invariant features. Intuitively,
diminishing the reliance of adversarial perturbations on the original data,
under equivalent attack performance constraints, encourages a greater reliance
on invariant features that contributes most to classification, thereby
enhancing the transferability of adversarial attacks. Building on this
motivation, we redefine the optimization of transferable attacks using a novel
theoretical framework that centers around IB. Specifically, to overcome the
challenge of unoptimizable mutual information, we propose a simple and
efficient mutual information lower bound (MILB) for approximating computation.
Moreover, to quantitatively evaluate mutual information, we utilize the Mutual
Information Neural Estimator (MINE) to perform a thorough analysis. Our
experiments on the ImageNet dataset well demonstrate the efficiency and
scalability of IBTA and derived MILB. Our code is available at
https://github.com/Biqing-Qi/Enhancing-Adversarial-Transferability-via-Information-Bottleneck-Constraints.
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关键词
Adversarial Transferability,Adversarial Attack,Information Bottleneck
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