Data-Driven Subsampling in the Presence of an Adversarial Actor
This work addresses security vulnerabilities in modulation classification for military and civilian applications, but it is incremental as it builds on existing subsampling methods.
The paper tackles the problem of adversarial attacks on deep learning-based automatic modulation classification systems that use data-driven subsampling, finding that subsampling itself effectively deters attacks and identifying the most efficient subsampling strategy under such threats.
Deep learning based automatic modulation classification (AMC) has received significant attention owing to its potential applications in both military and civilian use cases. Recently, data-driven subsampling techniques have been utilized to overcome the challenges associated with computational complexity and training time for AMC. Beyond these direct advantages of data-driven subsampling, these methods also have regularizing properties that may improve the adversarial robustness of the modulation classifier. In this paper, we investigate the effects of an adversarial attack on an AMC system that employs deep learning models both for AMC and for subsampling. Our analysis shows that subsampling itself is an effective deterrent to adversarial attacks. We also uncover the most efficient subsampling strategy when an adversarial attack on both the classifier and the subsampler is anticipated.