The ubiquity of social media platforms facilitates malicious linguistic steganography, posing significant security risks. However, detection is severely hampered by two fundamental issues during model training. Firstly, extreme class imbalance (less than 1% steganographic samples) induces a strong decision bias. Secondly, the invisibility of generative steganography means its features are nearly indistinguishable from benign text; this similarity, compounded by their extreme rarity, leads to severe feature marginalization, where faint steganographic signals are completely overwhelmed. To directly address these optimization-level challenges, we propose FADRW (Feature-Aware Modulated and Dynamically Reweighted Loss), a novel loss function framework engineered for few-shot steganalysis. FADRW employs Dynamic Reweighting to progressively counteract decision bias, and a Feature-Aware Modulation module to structurally reshape the feature space, preventing feature marginalization by enhancing the separability of these subtle features. Extensive experiments on datasets from three real-world social platforms demonstrate that FADRW significantly outperforms state-of-the-art methods, particularly in the challenging few-shot steganographic sample scenario.

翻译：摘要：社交媒体平台的普及助长了恶意语言隐写术的滋生，带来显著安全风险。然而，模型训练过程中的两个根本性问题严重制约了检测效果。其一，极端类别不平衡（隐写样本占比不足1%）导致强烈决策偏差；其二，生成式隐写的隐蔽性使其特征与正常文本近乎不可区分——这种相似性叠加极端稀缺性导致了严重的特征边缘化，致使微弱的隐写信号被完全淹没。为从优化层面直接应对这些挑战，我们提出FADRW（特征感知调制与动态重加权损失），一种专为少样本隐写分析设计的新型损失函数框架。该框架通过动态重加权逐步抵消决策偏差，并借助特征感知调制模块结构性重塑特征空间，通过增强微弱特征的可分离性防止特征边缘化。在来自三个真实社交平台的数据集上的大量实验表明，FADRW在极具挑战性的少样本隐写样本场景中显著优于现有最优方法。