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Optimization of Communication Signal Adversarial Examples by Selectively Preserving Low-Frequency Components of

Yi Zhang1,2, Lulu Wang1, Xiaolei Wang1

  • 1Intelligent Game and Decision Lab, Beijing 100071, China.

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Summary

This study introduces a new method to create better adversarial examples for communication signals by focusing on low-frequency components of perturbations (LFCP). The approach minimizes distortion while maintaining a high attack success rate (ASR).

Keywords:
adversarial examplescommunication signallow-frequencyperturbations

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Signal Processing

Background:

  • Adversarial examples in machine learning aim for high attack success rate (ASR) with minimal distortion.
  • Optimizing adversarial examples for communication signals is a significant challenge.

Purpose of the Study:

  • To propose a novel method for optimizing communication signal adversarial examples.
  • To focus on low-frequency components of perturbations (LFCP) for improved optimization.

Main Methods:

  • Analyzing model attention to DCT coefficients to understand the role of LFCP.
  • Developing an optimization strategy based on preserving LFCP.
  • Employing a binary search algorithm with model prediction inconsistency as a constraint to identify LFCP.

Main Results:

  • The proposed method significantly minimizes perturbed distortion for FGSM, BIM, PGD, and MI-FGSM attacks.
  • A modest improvement in ASR was observed with the proposed method.
  • The method shows feasible performance even for attacks like DeepFool and BS-FGM that use small perturbations.

Conclusions:

  • Preserving LFCP is a fundamental concept for optimizing adversarial examples in communication signals.
  • The binary search-based approach effectively balances minimizing distortion and maintaining ASR.
  • The method offers a practical solution for generating robust adversarial examples.