  Deep Neural Networks are well known to be vulnerable to adversarial attacks
and backdoor attacks, where minor modifications on the input are able to
mislead the models to give wrong results. Although defenses against adversarial
attacks have been widely studied, investigation on mitigating backdoor attacks
is still at an early stage. It is unknown whether there are any connections and
common characteristics between the defenses against these two attacks. We
conduct comprehensive studies on the connections between adversarial examples
and backdoor examples of Deep Neural Networks to seek to answer the question:
can we detect backdoor using adversarial detection methods. Our insights are
based on the observation that both adversarial examples and backdoor examples
have anomalies during the inference process, highly distinguishable from benign
samples. As a result, we revise four existing adversarial defense methods for
detecting backdoor examples. Extensive evaluations indicate that these
approaches provide reliable protection against backdoor attacks, with a higher
accuracy than detecting adversarial examples. These solutions also reveal the
relations of adversarial examples, backdoor examples and normal samples in
model sensitivity, activation space and feature space. This is able to enhance
our understanding about the inherent features of these two attacks and the
defense opportunities.
