Adversarial Attacks Against Double RIS-Assisted MIMO Systems-based Autoencoder in Finite-Scattering Environments

TL;DR

This paper investigates the vulnerability of double RIS-assisted MIMO autoencoders to adversarial attacks, proposing algorithms that significantly impair system performance by exploiting gradient-based perturbations.

Contribution

It introduces three novel algorithms for generating adversarial examples against RIS-MIMO autoencoders, highlighting their effectiveness over traditional jamming methods.

Findings

Adversarial attacks greatly increase symbol error rates.

Proposed algorithms outperform jamming attacks in degrading performance.

Flexibility in attack input dimensions enhances attack effectiveness.

Abstract

Autoencoder permits the end-to-end optimization and design of wireless communication systems to be more beneficial than traditional signal processing. However, this emerging learning-based framework has weaknesses, especially sensitivity to physical attacks. This paper explores adversarial attacks against a double reconfigurable intelligent surface (RIS)-assisted multiple-input and multiple-output (MIMO)-based autoencoder, where an adversary employs encoded and decoded datasets to create adversarial perturbation and fool the system. Because of the complex and dynamic data structures, adversarial attacks are not unique, each having its own benefits. We, therefore, propose three algorithms generating adversarial examples and perturbations to attack the RIS-MIMO-based autoencoder, exploiting the gradient descent and allowing for flexibility via varying the input dimensions. Numerical results show that the proposed adversarial attack-based algorithm significantly degrades the system performance regarding the symbol error rate compared to the jamming attacks.