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arXiv AI By Florian Eilers, Christof Duhme, Xiaoyi Jiang

Perturbing the Phase: Analyzing Adversarial Robustness of Complex-Valued Neural Networks

Read the original on arXiv AI →

The paper introduces Phase Attacks, a novel adversarial technique that targets the phase component of complex-valued inputs in complex-valued neural networks (CVNNs). It also extends traditional adversarial attacks to the complex domain and compares CVNNs with real-valued neural networks (RVNNs). The results show that CVNNs can be more robust in some cases, yet both architectures are highly vulnerable to phase perturbations, with Phase Attacks causing greater performance degradation than equivalent magnitude‑based attacks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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