An Adversarial Attack against Stacked Capsule Autoencoder
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Capsule network is a kind of neural network which uses spatial relationship between features to classify images. By capturing poses and relative positions between features, its ability to recognize affine transformation is improved and surpasses traditional convolutional neural networks (CNNs) when dealing with translation, rotation and scaling. Stacked Capsule Autoencoder (SCAE) is the state-of-the-art generation of capsule network. SCAE encodes the image as capsules, each of which contains poses of features and their correlations. The encoded contents are then input into downstream classifier to predict the categories of the images. Existed research mainly focuses on security of capsule networks with dynamic routing or EM routing, little attention has been paid to the security and robustness of SCAE. In this paper, we propose an evasion attack against SCAE. After perturbation is generated with an optimization algorithm, it is added to an image to reduce the output of capsules related to the original category of the image. As the contribution of these capsules to the original class is reduced, the perturbed image will be misclassified. We evaluate the attack with image classification experiment on the MNIST dataset. The experimental results indicate that our attack can achieve around 99
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