Unveiling Vulnerabilities in Interpretable Deep Learning Systems with
  Query-Efficient Black-box Attacks
Eldor Abdukhamidov Mohammed Abuhamad Simon S. Woo Eric Chan-Tin Tamer Abuhmed
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.