论文标题
集合噪声模拟以处理基于梯度的对抗攻击的不确定性
Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks
论文作者
论文摘要
可以通过各种方式来制定对神经网络的基于梯度的对抗性攻击，从而改变了攻击算法如何依赖梯度，用于制作攻击的网络体系结构或两者兼而有之。在攻击者行为不确定性的情况下，最近的工作集中在为分类器辩护（即，预计攻击者将使用特定的网络体系结构生成特定的攻击）。但是，如果不能保证攻击者以某种方式行事，那么文献缺乏设计战略辩护的方法。我们通过使用基于各种分类器梯度的各种攻击算法模拟攻击者的嘈杂扰动来填补这一空白。我们使用经过模拟噪声训练的预处理Denoising AutoCododer（DAE）防御进行分析。与没有努力处理不确定性的情况相比，使用我们提出的合奏培训的防御，我们证明了攻击后准确性的显着提高。
Gradient-based adversarial attacks on neural networks can be crafted in a variety of ways by varying either how the attack algorithm relies on the gradient, the network architecture used for crafting the attack, or both. Most recent work has focused on defending classifiers in a case where there is no uncertainty about the attacker's behavior (i.e., the attacker is expected to generate a specific attack using a specific network architecture). However, if the attacker is not guaranteed to behave in a certain way, the literature lacks methods in devising a strategic defense. We fill this gap by simulating the attacker's noisy perturbation using a variety of attack algorithms based on gradients of various classifiers. We perform our analysis using a pre-processing Denoising Autoencoder (DAE) defense that is trained with the simulated noise. We demonstrate significant improvements in post-attack accuracy, using our proposed ensemble-trained defense, compared to a situation where no effort is made to handle uncertainty.
