Increasing popularity of deep-learning-powered applications raises the issue
of vulnerability of neural networks to adversarial attacks. In other words,
hardly perceptible changes in input data lead to the output error in neural
network hindering their utilization in applications that involve decisions with
security risks. A number of previous works have already thoroughly evaluated
the most commonly used configuration - Convolutional Neural Networks (CNNs)
against different types of adversarial attacks. Moreover, recent works
demonstrated transferability of the some adversarial examples across different
neural network models. This paper studied robustness of the new emerging models
such as SpinalNet-based neural networks and Compact Convolutional Transformers
(CCT) on image classification problem of CIFAR-10 dataset. Each architecture
was tested against four White-box attacks and three Black-box attacks. Unlike
VGG and SpinalNet models, attention-based CCT configuration demonstrated large
span between strong robustness and vulnerability to adversarial examples.
Eventually, the study of transferability between VGG, VGG-inspired SpinalNet
and pretrained CCT 7/3x1 models was conducted. It was shown that despite high
effectiveness of the attack on the certain individual model, this does not
guarantee the transferability to other models.Comment: 18 page