Deep learning has been rapidly employed in many applications revolutionizing
many industries, but it is known to be vulnerable to adversarial attacks. Such
attacks pose a serious threat to deep learning-based systems compromising their
integrity, reliability, and trust. Interpretable Deep Learning Systems (IDLSes)
are designed to make the system more transparent and explainable, but they are
also shown to be susceptible to attacks. In this work, we propose a novel
microbial genetic algorithm-based black-box attack against IDLSes that requires
no prior knowledge of the target model and its interpretation model. The
proposed attack is a query-efficient approach that combines transfer-based and
score-based methods, making it a powerful tool to unveil IDLS vulnerabilities.
Our experiments of the attack show high attack success rates using adversarial
examples with attribution maps that are highly similar to those of benign
samples which makes it difficult to detect even by human analysts. Our results
highlight the need for improved IDLS security to ensure their practical
reliability.

我们提出了一种基于微生物遗传算法的黑盒攻击方法，用于攻击解释性深度学习系统（IDLSes），该方法不需要目标模型和解释模型的先验知识，且通过结合基于转移和基于分数的方法，攻击成功率高，难以被人类分析人员检测，突出了 IDLS 安全性的提升需求。