Exploring Transferability on Adversarial Attacks

Enrique Álvarez

doi:10.1109/ACCESS.2023.3319389

2023-01-01

Enrique Álvarez *

Rafael Álvarez

Miguel Cazorla

DOI：10.1109/ACCESS.2023.3319389

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摘要

En 中文

Despite of the progress that has been made in the field, the problem of adversarial attacks remains unresolved. The most up-to-date models are still vulnerable, and there is not a simple way to defend against these kinds of attacks; even transformers can be affected by this problem, although they have not been extensively studied yet. In this paper, we study transferability, which is a property of adversarial attacks in which images generated for one architecture can be transferred to another and still be effective. In real-world scenarios like self-driving cars, malware detection, and face recognition authentication systems, transferability can lead to security issues. In order to conduct a behavioral analysis, we select a diverse set of networks and measure how effectively the images produced by various attacks can be transferred among them. We generate adversarial samples for each network and then evaluate them with other networks to determine the corresponding transferability performance. We can observe that all networks are susceptible to transferability attacks, albeit in some cases at the expense of severely distorted images.

Keyword:

Glass box

Residual neural networks

Closed box

Subspace constraints

Convolutional neural networks

Iterative methods

Adversarial machine learning

Deep learning

Information management

Adversarial attacks

convolutional neural networks