Golden Ratio Search: A Low-Power Adversarial Attack for Deep Learning based Modulation Classification
This work addresses security vulnerabilities in wireless communication systems, presenting an incremental improvement in adversarial attack efficiency for modulation classification.
The paper tackles the problem of adversarial attacks on deep learning-based automatic modulation classification by proposing a low-power attack using the Golden Ratio Search method, achieving powerful attacks with minimal power and faster generation times.
We propose a minimal power white box adversarial attack for Deep Learning based Automatic Modulation Classification (AMC). The proposed attack uses the Golden Ratio Search (GRS) method to find powerful attacks with minimal power. We evaluate the efficacy of the proposed method by comparing it with existing adversarial attack approaches. Additionally, we test the robustness of the proposed attack against various state-of-the-art architectures, including defense mechanisms such as adversarial training, binarization, and ensemble methods. Experimental results demonstrate that the proposed attack is powerful, requires minimal power, and can be generated in less time, significantly challenging the resilience of current AMC methods.