Spectrum Focused Frequency Adversarial Attacks for Automatic Modulation Classification

08/03/2022
by   Sicheng Zhang, et al.
0

Artificial intelligence (AI) technology has provided a potential solution for automatic modulation recognition (AMC). Unfortunately, AI-based AMC models are vulnerable to adversarial examples, which seriously threatens the efficient, secure and trusted application of AI in AMC. This issue has attracted the attention of researchers. Various studies on adversarial attacks and defenses evolve in a spiral. However, the existing adversarial attack methods are all designed in the time domain. They introduce more high-frequency components in the frequency domain, due to abrupt updates in the time domain. For this issue, from the perspective of frequency domain, we propose a spectrum focused frequency adversarial attacks (SFFAA) for AMC model, and further draw on the idea of meta-learning, propose a Meta-SFFAA algorithm to improve the transferability in the black-box attacks. Extensive experiments, qualitative and quantitative metrics demonstrate that the proposed algorithm can concentrate the adversarial energy on the spectrum where the signal is located, significantly improve the adversarial attack performance while maintaining the concealment in the frequency domain.

READ FULL TEXT

page 1

page 4

research
03/29/2022

Exploring Frequency Adversarial Attacks for Face Forgery Detection

Various facial manipulation techniques have drawn serious public concern...
research
06/14/2020

Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems

Adversarial attacks pose significant challenges for detecting adversaria...
research
07/12/2022

Frequency Domain Model Augmentation for Adversarial Attack

For black-box attacks, the gap between the substitute model and the vict...
research
10/29/2020

WaveTransform: Crafting Adversarial Examples via Input Decomposition

Frequency spectrum has played a significant role in learning unique and ...
research
12/24/2018

Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks

We consider adversarial examples in the black-box decision-based scenari...
research
02/21/2023

Interpretable Spectrum Transformation Attacks to Speaker Recognition

The success of adversarial attacks to speaker recognition is mainly in w...
research
04/19/2022

Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors

Deepfakes utilise Artificial Intelligence (AI) techniques to create synt...

Please sign up or login with your details

Forgot password? Click here to reset