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.

翻译：深度学习的最新进展催生了基于人工智能的纠错解码器，据报道其在加性高斯白噪声信道上的经验性能优于传统的置信传播解码。尽管这些性能提升令人鼓舞，但一个根本问题依然存在：这些改进从何而来，为实现它们付出了何种代价？在本工作中，我们通过信道输出分布偏移的鲁棒性视角来研究这一问题。我们评估了输入相关的对抗性扰动（在$\ell_2$约束下的快速梯度法和投影梯度法）以及适用于所有接收向量的单一范数有界偏移的通用对抗性扰动。我们的结果表明，包括ECCT和CrossMPT在内的近期人工智能解码器在此类扰动下可能遭受显著的性能下降，尽管其在独立同分布加性高斯白噪声条件下具有优异的标称性能。此外，对抗性扰动在人工智能解码器之间的迁移性相对较强，但对基于置信传播的解码器迁移性较弱；且同等范数下，通用扰动比随机扰动的危害性大得多。这些数值发现表明，近期人工智能解码性能的提升可能伴随着潜在的鲁棒性代价以及对信道分布更高的敏感性。