论文标题
在专门的深神经网络合奏中，通过多样性来实现对抗性的鲁棒性
Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks
论文作者
论文摘要
我们旨在证明多样性在CNN集合中对检测黑盒对抗实例的影响并加强了白盒对抗攻击的产生。为此，我们提出了各种专业CNN的合奏以及简单的投票机制。这种合奏中的多样性在对手的预测信心与干净样本的预测信心之间存在差距，从而使对手可检测到。然后，我们分析了这样一组专家的多样性如何减轻黑盒和白盒对抗性示例的风险。使用MNIST和CIFAR-10，我们凭经验验证了合奏检测大部分众所周知的黑盒对手实例的能力，这导致对手风险率显着降低，而牺牲了清洁样品的风险率很小。此外，我们表明，与香草CNN和香草CNN的合奏相比，我们的合奏产生白盒攻击的成功率明显降低，这突出了多样性在开发更强大模型的合奏中的有益作用。
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.
