An Evasion Attack against Stacked Capsule Autoencoder
Jiazhu Dai, Siwei Xiong

TL;DR
This paper presents a novel evasion attack on the Stacked Capsule Autoencoder (SCAE), revealing its vulnerability to adversarial perturbations that can cause misclassification without altering the original image structure.
Contribution
The paper introduces the first evasion attack targeting SCAE, demonstrating its security weaknesses and potential for adversarial exploitation.
Findings
High success rate of the attack in fooling SCAE
Stealthy perturbations that do not alter image structure
SCAE's vulnerability to adversarial samples
Abstract
Capsule network is a type of neural network that uses the spatial relationship between features to classify images. By capturing the poses and relative positions between features, its ability to recognize affine transformation is improved, and it surpasses traditional convolutional neural networks (CNNs) when handling translation, rotation and scaling. The Stacked Capsule Autoencoder (SCAE) is the state-of-the-art capsule network. The SCAE encodes an image as capsules, each of which contains poses of features and their correlations. The encoded contents are then input into the downstream classifier to predict the categories of the images. Existing research mainly focuses on the security of capsule networks with dynamic routing or EM routing, and little attention has been given to the security and robustness of the SCAE. In this paper, we propose an evasion attack against the SCAE. After…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications · Advanced Malware Detection Techniques
MethodsSolana Customer Service Number +1-833-534-1729
