One of the tasks of law enforcement agencies is to find evidence of criminal
activity in the Darknet. However, visiting thousands of domains to locate
visual information containing illegal acts manually requires a considerable
amount of time and resources. Furthermore, the background of the images can
pose a challenge when performing classification. To solve this problem, in this
paper, we explore the automatic classification Tor Darknet images using
Semantic Attention Keypoint Filtering, a strategy that filters non-significant
features at a pixel level that do not belong to the object of interest, by
combining saliency maps with Bag of Visual Words (BoVW). We evaluated SAKF on a
custom Tor image dataset against CNN features: MobileNet v1 and Resnet50, and
BoVW using dense SIFT descriptors, achieving a result of 87.98% accuracy and
outperforming all other approaches.

本文探讨了利用语义关注关键点过滤自动分类暗网图像的方法，并在定制的 Tor 图像数据集上使用该方法测试，与 CNN 特征和 BoVW 方法相比，达到了高达 87.98% 的准确率，是一种较为有效的方式。

Tor 暗网中可疑内容的分类

Classifying Suspicious Content in Tor Darknet

Organized crime inflicts human suffering on a genocidal scale: the Mexican
drug cartels have murdered 150,000 people since 2006, upwards of 700,000 people
per year are "exported" in a human trafficking industry enslaving an estimated
40 million people. These nefarious industries rely on sophisticated money
laundering schemes to operate. Despite tremendous resources dedicated to
anti-money laundering (AML) only a tiny fraction of illicit activity is
prevented. The research community can help. In this brief paper, we map the
structural and behavioral dynamics driving the technical challenge. We review
AML methods, current and emergent. We provide a first look at scalable graph
convolutional neural networks for forensic analysis of financial data, which is
massive, dense, and dynamic. We report preliminary experimental results using a
large synthetic graph (1M nodes, 9M edges) generated by a data simulator we
created called AMLSim. We consider opportunities for high performance
efficiency, in terms of computation and memory, and we share results from a
simple graph compression experiment. Our results support our working hypothesis
that graph deep learning for AML bears great promise in the fight against
criminal financial activity.

该研究论文探讨了有组织犯罪对人类社会带来的巨大伤害，特别是针对金融领域的洗钱活动。研究者提出了一种新方法，使用大规模图形深度学习对涉嫌犯罪的金融数据进行犯罪活动监测，实验结果表明该方法具有很大的应用前景。