论文标题
对抗性幅度互换向强大的图像分类器
Adversarial amplitude swap towards robust image classifiers
论文作者
论文摘要
最近从频率的角度研究了卷积神经网络（CNN）对图像扰动（例如常见腐败和对抗扰动）的脆弱性。在这项研究中，我们研究了对抗图像的幅度和相光谱对CNN分类器鲁棒性的影响。广泛的实验表明，通过结合对抗图像的振幅光谱而产生的图像和清洁图像的相光谱可容纳中等和一般的扰动，并使用这些图像训练CNN分类器使CNN分类器具有更一般的鲁棒性，在常见的腐败和对抗性的危险下表现良好。我们还发现，可以通过上述频谱重组来规避两种类型的过拟合（灾难性的过度拟合和可靠的过度拟合）。我们认为，这些结果有助于对真正强大的分类器的理解和培训。
The vulnerability of convolutional neural networks (CNNs) to image perturbations such as common corruptions and adversarial perturbations has recently been investigated from the perspective of frequency. In this study, we investigate the effect of the amplitude and phase spectra of adversarial images on the robustness of CNN classifiers. Extensive experiments revealed that the images generated by combining the amplitude spectrum of adversarial images and the phase spectrum of clean images accommodates moderate and general perturbations, and training with these images equips a CNN classifier with more general robustness, performing well under both common corruptions and adversarial perturbations. We also found that two types of overfitting (catastrophic overfitting and robust overfitting) can be circumvented by the aforementioned spectrum recombination. We believe that these results contribute to the understanding and the training of truly robust classifiers.
