Anti-ESIA: Analyzing and Mitigating Impacts of Electromagnetic Signal Injection Attacks

TL;DR

This paper investigates Electromagnetic Signal Injection Attacks (ESIA) on cameras, analyzing how they cause pixel loss and color distortions, and proposes a lightweight mitigation method to enhance camera security in intelligent systems.

Contribution

It provides a detailed analysis of ESIA effects on images and introduces a novel, lightweight countermeasure to mitigate these attacks.

Findings

ESIA causes significant pixel loss and color distortions.

Analyzed effects on image classification tasks.

Proposed a lightweight mitigation approach.

Abstract

Cameras are integral components of many critical intelligent systems. However, a growing threat, known as Electromagnetic Signal Injection Attacks (ESIA), poses a significant risk to these systems, where ESIA enables attackers to remotely manipulate images captured by cameras, potentially leading to malicious actions and catastrophic consequences. Despite the severity of this threat, the underlying reasons for ESIA's effectiveness remain poorly understood, and effective countermeasures are lacking. This paper aims to address these gaps by investigating ESIA from two distinct aspects: pixel loss and color strips. By analyzing these aspects separately on image classification tasks, we gain a deeper understanding of how ESIA can compromise intelligent systems. Additionally, we explore a lightweight solution to mitigate the effects of ESIA while acknowledging its limitations. Our findings provide valuable insights for future research and development in the field of camera security and intelligent systems.