The DarkWeb represents a hotbed for illicit activity, where users communicate on different market forums in order to exchange goods and services. Law enforcement agencies benefit from forensic tools that perform authorship analysis, in order to identify and profile users based on their textual content. However, authorship analysis has been traditionally studied using corpora featuring literary texts such as fragments from novels or fan fiction, which may not be suitable in a cybercrime context. Moreover, the few works that employ authorship analysis tools for cybercrime prevention usually employ ad-hoc experimental setups and datasets. To address these issues, we release VeriDark: a benchmark comprised of three large scale authorship verification datasets and one authorship identification dataset obtained from user activity from either Dark Web related Reddit communities or popular illicit Dark Web market forums. We evaluate competitive NLP baselines on the three datasets and perform an analysis of the predictions to better understand the limitations of such approaches. We make the datasets and baselines publicly available at https://github.com/bit-ml/VeriDark

翻译：黑暗网络是非法活动的温床,用户在不同的市场论坛上进行交流,以交换货物和服务;执法机构受益于法证工具,进行作者分析,以便根据作者的文字内容识别和描述用户;然而,传统上,对作者分析的研究使用了以文学作品如小说或影迷小说或小说小说小说小说小说小说或小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说,在网上可能不合适;此外,利用作者分析预防网络犯罪小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说小说