One of the tasks Law Enforcement Agencies are responsible for is to find evidence of criminal activities in the Darknet. However, visiting thousands of domains to locate visual information containing illicit acts manually requires a considerable amount of time and human resources. To support this task, in this paper, we explore the automatic classification of images uploaded to Tor darknet.

Unfortunately, the foreground objects on such images are not always presented standalone, without background, due to the environmental conditions. To address this challenge on the digital investigation of Tor darknet visual content, we propose to classify automatically only relevant parts of the image combining saliency maps, i.e. to select the regions with the most salient information, with Bag of Visual Words (BoVW). We introduce Semantic Attention Keypoint Filtering (SAKF), a filtering strategy that removes non-significant features at a pixel level that mainly do not belong to the object of interest or foreground. We assessed SAKF on seven publicly available datasets, obtaining from 1.64 to 15.73 points higher accuracies than the method set as the baseline, i.e. BoVW using dense SIFT (Scale-Invariant Feature Transform) descriptors. We also compared SAKF filtering performance against the deep features extracted from two well-known Convolutional Neural Network (CNN) architectures, namely MobileNet and ResNet50.

Experimental results reveal the effectiveness of the proposed approach and highlight that the use of automatic image classification could be advantageous to support daily Law Enforcement Agencies investigations on Tor darknet.

执法机构负责的任务之一是在Darknet中查找犯罪活动的证据。但是，访问成千上万个域来手动查找包含非法行为的视觉信息需要大量的时间和人力资源。为了支持此任务，在本文中，我们探索了上传到Tor darknet的图像的自动分类。

不幸的是，由于环境条件的原因，这些图像上的前景对象并非总是独立显示，没有背景。为了解决对Tor暗网视觉内容进行数字调查的挑战，我们建议结合显着图自动对图像的相关部分进行自动分类，即使用视觉词袋（BoVW）选择信息最突出的区域。我们引入了语义注意关键点过滤（SAKF），这是一种过滤策略，可在像素级别上删除主要不属于感兴趣或前景对象的非重要特征。我们在七个公开可用的数据集上评估了SAKF，其准确度比设置为基准的方法（即BoVW使用密集的SIFT（尺度不变特征变换）描述符）高1.64至15.73点。

实验结果证明了该方法的有效性，并强调了自动图像分类的使用可能有利于支持对Tor Darknet的日常执法机构调查。