In social network service platforms, crime suspects are likely to use
cybercrime coded words for communication by adding criminal meanings to
existing words or replacing them with similar words. For instance, the word
'ice' is often used to mean methamphetamine in drug crimes. To analyze the
nature of cybercrime and the behavior of criminals, quickly detecting such
words and further understanding their meaning are critical. In the automated
cybercrime coded word detection problem, it is difficult to collect a
sufficient amount of training data for supervised learning and to directly
apply language models that utilize context information to better understand
natural language. To overcome these limitations, we propose a new two-step
approach, in which a mean latent vector is constructed for each cybercrime
through one of five different AutoEncoder models in the first step, and
cybercrime coded words are detected based on multi-level latent representations
in the second step. Moreover, to deeply understand cybercrime coded words
detected through the two-step approach, we propose three novel methods: (1)
Detection of new words recently coined, (2) Detection of words frequently
appeared in both drug and sex crimes, and (3) Automatic generation of word
taxonomy. According to our experimental results, among various AutoEncoder
models, the stacked AutoEncoder model shows the best performance. Additionally,
the F1-score of the two-step approach is 0.991, which is higher than 0.987 and
0.903 of the existing dark-GloVe and dark-BERT models. By analyzing the
experimental results of the three proposed methods, we can gain a deeper
understanding of drug and sex crimes.

通过多级潜在表示及自动编码器模型，我们提出了一种新的两步骤方法来检测犯罪行为中的网络犯罪编码词，并通过分析实验结果深入了解毒品和性犯罪。

多级表示学习的两步自动网络犯罪密码词检测

Two-step Automated Cybercrime Coded Word Detection using Multi-level  Representation Learning

This paper proposes exploiting the common sense knowledge learned by large
language models to perform zero-shot reasoning about crimes given textual
descriptions of surveillance videos. We show that when video is (manually)
converted to high quality textual descriptions, large language models are
capable of detecting and classifying crimes with state-of-the-art performance
using only zero-shot reasoning. However, existing automated video-to-text
approaches are unable to generate video descriptions of sufficient quality to
support reasoning (garbage video descriptions into the large language model,
garbage out).

本文提出利用语言模型学习的常识知识，对监控视频文字描述进行零 - shot 推理，能够实现仅使用零 - shot 推理对犯罪进行检测和分类的性能优秀于现有的视频到文字自动生成模型。

垃圾进，垃圾出：使用大型语言模型进行零样本犯罪检测

Garbage in, garbage out: Zero-shot detection of crime using Large  Language Models

This paper describes research on the detection of Polish criminal texts
appearing on the Internet. We carried out experiments to find the best
available setup for the efficient classification of unbalanced and noisy data.
The best performance was achieved when our model was fine-tuned on a
pre-trained Polish-based transformer language model. For the detection task, a
large corpus of annotated Internet snippets was collected as training data. We
share this dataset and create a new task for the detection of criminal texts
using the Gonito platform as the benchmark.

研究波兰犯罪文本在互联网上的检测，使用预训练的波兰语变形器语言模型进行微调得到最佳性能；使用大型标注的互联网片段语料库作为训练数据，并在 Gonito 平台上创建了一个新的犯罪文本检测任务。