The prominent success of neural networks, mainly in computer vision tasks, is
increasingly shadowed by their sensitivity to small, barely perceivable
adversarial perturbations in image input.
In this work, we aim at explaining this vulnerability through the framework
of sparsity.
We show the connection between adversarial attacks and sparse
representations, with a focus on explaining the universality and
transferability of adversarial examples in neural networks.
To this end, we show that sparse coding algorithms, and the neural
network-based learned iterative shrinkage thresholding algorithm (LISTA) among
them, suffer from this sensitivity, and that common attacks on neural networks
can be expressed as attacks on the sparse representation of the input image.
The phenomenon that we observe holds true also when the network is agnostic to
the sparse representation and dictionary, and thus can provide a possible
explanation for the universality and transferability of adversarial attacks.
The code is available at
this https URL

通过稀疏性框架，本文研究了神经网络在计算机视觉任务中的敏感性以及与对抗性攻击的联系，揭示了对输入图像稀疏表示的攻击可以作为对神经网络的常见攻击表达，进一步解释了对抗性攻击的普遍性和可传递性。