针对少样本隐写检测中信号微弱与数据不均衡问题，提出新型损失函数提升检测效果。
FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis
- 设计动态重加权与特征感知调制机制，缓解分类偏倚和特征淹没。
- 在真实社交平台数据上，少样本场景下检测准确率显著超越现有方法。
- 适合需要高精度隐写检测的网络安全与内容审核场景使用。
社交媒体平台的普及助长了恶意语言隐写，带来重大安全风险。然而，模型训练面临两大根本性挑战：一是极端类别不平衡（隐写样本不足1%），导致严重决策偏倚；二是生成式隐写的不可见性使其特征几乎与正常文本无法区分，这种相似性叠加极端稀有性，造成特征边缘化，微弱隐写信号被完全掩盖。为直接应对这些优化层面的问题，我们提出FADRW（Feature-Aware Modulated and Dynamically Reweighted Loss）——一种专为少样本隐写分析设计的新颖损失函数框架。FADRW通过动态重加权逐步对抗决策偏倚，并引入特征感知调制模块，从结构上重塑特征空间，增强微弱特征的可分性，防止特征边缘化。在三个真实社交平台数据集上的大量实验表明，FADRW显著优于当前最先进方法，尤其在极具挑战性的少样本隐写样本场景中表现突出。
原文摘要 · Abstract (English)
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.
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