  The traditional convolution neural networks (CNN) have several drawbacks like
the Picasso effect and the loss of information by the pooling layer. The
Capsule network (CapsNet) was proposed to address these challenges because its
architecture can encode and preserve the spatial orientation of input images.
Similar to traditional CNNs, CapsNet is also vulnerable to several malicious
attacks, as studied by several researchers in the literature. However, most of
these studies focus on single-device-based inference, but horizontally
collaborative inference in state-of-the-art systems, like intelligent edge
services in self-driving cars, voice controllable systems, and drones, nullify
most of these analyses. Horizontal collaboration implies partitioning the
trained CNN models or CNN tasks to multiple end devices or edge nodes.
Therefore, it is imperative to examine the robustness of the CapsNet against
malicious attacks when deployed in horizontally collaborative environments.
Towards this, we examine the robustness of the CapsNet when subjected to
noise-based inference attacks in a horizontal collaborative environment. In
this analysis, we perturbed the feature maps of the different layers of four
DNN models, i.e., CapsNet, Mini-VGG, LeNet, and an in-house designed CNN
(ConvNet) with the same number of parameters as CapsNet, using two types of
noised-based attacks, i.e., Gaussian Noise Attack and FGSM noise attack. The
experimental results show that similar to the traditional CNNs, depending upon
the access of the attacker to the DNN layer, the classification accuracy of the
CapsNet drops significantly. For example, when Gaussian Noise Attack
classification is performed at the DigitCap layer of the CapsNet, the maximum
classification accuracy drop is approximately 97%.
