On the Fragility of AI-Based Channel Decoders under Small Channel Perturbations

TL;DR

This paper investigates the robustness of AI-based channel decoders against small adversarial perturbations, revealing significant performance degradation and highlighting a potential robustness cost associated with their empirical gains over traditional methods.

Contribution

It provides the first analysis of the robustness of AI decoders under adversarial channel perturbations, exposing their vulnerability compared to belief-propagation decoders.

Findings

AI decoders suffer performance loss under adversarial perturbations

Adversarial perturbations transfer between AI decoders but not to BP decoders

Universal perturbations are more damaging than random noise of the same norm

Abstract

Recent advances in deep learning have led to AI-based error correction decoders that report empirical performance improvements over traditional belief-propagation (BP) decoding on AWGN channels. While such gains are promising, a fundamental question remains: where do these improvements come from, and what cost is paid to achieve them? In this work, we study this question through the lens of robustness to distributional shifts at the channel output. We evaluate both input-dependent adversarial perturbations (FGM and projected gradient methods under $\ell_2$ constraints) and universal adversarial perturbations that apply a single norm-bounded shift to all received vectors. Our results show that recent AI decoders, including ECCT and CrossMPT, could suffer significant performance degradation under such perturbations, despite superior nominal performance under i.i.d. AWGN. Moreover, adversarial perturbations transfer relatively strongly between AI decoders but weakly to BP-based decoders, and universal perturbations are substantially more harmful than random perturbations of equal norm. These numerical findings suggest a potential robustness cost and higher sensitivity to channel distribution underlying recent AI decoding gains.