Maintaining anonymity while communicating using natural language remains a challenge. Standard authorship attribution techniques that analyze candidate authors' writing styles achieve uncomfortably high accuracy even when the number of candidate authors is high. Adversarial stylometry defends against authorship attribution with the goal of preventing unwanted deanonymization. This paper reproduces and replicates experiments in a seminal study of defenses against authorship attribution (Brennan et al., 2012). We are able to successfully reproduce and replicate the original results, although we conclude that the effectiveness of the defenses studied is overstated due to a lack of a control group in the original study. In our replication, we find new evidence suggesting that an entirely automatic method, round-trip translation, merits re-examination as it appears to reduce the effectiveness of established authorship attribution methods.
翻译:在使用自然语言进行沟通时保持匿名仍然是一个挑战。 分析候选作者写作风格的标准作者归属技术即使在候选作者数量高的情况下也达到了令人不适的高度准确性。 反逆性tytylologismation为反对作者归属而进行辩护,目的是防止不必要的去匿名化。 本文复制并复制了针对作者归属进行辩护的开创性研究的实验(Brennan等人,2012年)。 我们成功地复制和复制了原始结果,尽管我们的结论是,所研究的辩护的有效性被夸大了,因为原始研究中缺乏一个控制小组。 在复制过程中,我们发现了新的证据表明,一种完全自动的方法,即循环翻译,值得重新审查,因为它似乎降低了既定作者归属方法的有效性。