This paper aimed to discover the risks associated with the dark web and to detect the threats related to human trafficking using image processing with OpenCV and Python. Apart from that, a development environment was set up by installing TensorFlow, OpenCV and Python. Through exploratory data analysis (EDA), significant insights into the distribution and interactions of dataset features were obtained, which are crucial for evaluating various cyberthreats. The construction and evaluation of logistic regression and support vector machine (SVM) models revealed that the SVM model outperforms logistic regression in accuracy. The paper delves into the intricacies of data preprocessing, EDA, and model development, offering valuable insights into network protection and cyberthreat response.

翻译：本文旨在探究暗网相关风险，并利用OpenCV与Python的图像处理技术检测人口贩卖相关威胁。此外，通过安装TensorFlow、OpenCV与Python搭建了开发环境。通过探索性数据分析（EDA），获得了关于数据集特征分布与交互关系的重要洞见，这对评估各类网络威胁至关重要。逻辑回归与支持向量机（SVM）模型的构建与评估表明，SVM模型在准确率上优于逻辑回归。本文深入探讨了数据预处理、EDA与模型开发的复杂细节，为网络防护与网络威胁应对提供了有价值的见解。