We aim at demonstrating the influence of diversity in the ensemble of CNNs on
the detection of black-box adversarial instances and hardening the generation
of white-box adversarial attacks. To this end, we propose an ensemble of
diverse specialized CNNs along with a simple voting mechanism. The diversity in
this ensemble creates a gap between the predictive confidences of adversaries
and those of clean samples, making adversaries detectable. We then analyze how
diversity in such an ensemble of specialists may mitigate the risk of the
black-box and white-box adversarial examples. Using MNIST and CIFAR-10, we
empirically verify the ability of our ensemble to detect a large portion of
well-known black-box adversarial examples, which leads to a significant
reduction in the risk rate of adversaries, at the expense of a small increase
in the risk rate of clean samples. Moreover, we show that the success rate of
generating white-box attacks by our ensemble is remarkably decreased compared
to a vanilla CNN and an ensemble of vanilla CNNs, highlighting the beneficial
role of diversity in the ensemble for developing more robust models.

本文研究了利用多样的专业 CNNs 集成对黑盒对抗实例检测的影响，并加强白盒对抗攻击的生成，证明了不同专业集成的多样性如何减轻黑盒和白盒对抗示例的风险，并通过 MNIST 和 CIFAR-10 等实验证明了使用该集成可以检测大部分已知的黑盒对抗实例，从而显著降低敌人的风险率，但会在一定程度上增加干净样本的风险率。此外，相对于普通 CNN 和普通 CNN 集成，我们展示了集成生成白盒攻击的成功率显著下降，突显了集成中多样性对于开发更健壮模型的有益作用。