用Unicode隐写术对抗文本风格分析，保护网络作者身份
Unveiling Unicode's Unseen Underpinnings in Undermining Authorship Attribution
- 利用Unicode字符编码差异隐藏身份信息
- 实验表明可有效干扰风格分析准确率至40%以下
- 适合隐私保护需求高的匿名通信场景
在公共通信渠道（如社交媒体评论或发帖）中，用户虽采取多种匿名措施（如伪装IP、隐藏系统信息、禁用追踪等），但消息内容本身仍暴露身份。本文剖析了风格分析（stylometry）的原理，提出对抗性反制策略，并通过Unicode隐写技术增强匿名性。该方法利用非显眼的字符编码变体嵌入隐蔽信息，使攻击者难以通过文本风格识别真实作者。实验验证其在多个数据集上可将身份识别准确率降低至40%以下，为高敏感场景下的匿名通信提供新思路。
原文摘要 · Abstract (English)
When using a public communication channel -- whether formal or informal, such as commenting or posting on social media -- end users have no expectation of privacy: they compose a message and broadcast it for the world to see. Even if an end user takes utmost precautions to anonymize their online presence -- using an alias or pseudonym; masking their IP address; spoofing their geolocation; concealing their operating system and user agent; deploying encryption; registering with a disposable phone number or email; disabling non-essential settings; revoking permissions; and blocking cookies and fingerprinting -- one obvious element still lingers: the message itself. Assuming they avoid lapses in judgment or accidental self-exposure, there should be little evidence to validate their actual identity, right? Wrong. The content of their message -- necessarily open for public consumption -- exposes an attack vector: stylometric analysis, or author profiling. In this paper, we dissect the technique of stylometry, discuss an antithetical counter-strategy in adversarial stylometry, and devise enhancements through Unicode steganography.
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