论文标题
在tor darknet中对可疑内容进行分类
Classifying Suspicious Content in Tor Darknet
论文作者
论文摘要
执法机构的任务之一是在Darknet中找到犯罪活动的证据。但是，访问数千个域来找到包含非法行为的视觉信息需要大量的时间和资源。此外，在执行分类时，图像的背景可能会构成挑战。为了解决这个问题，在本文中，我们使用语义注意点击点过滤探索了自动分类tor darknet图像，该策略通过将显着性图与视觉单词（BOVW）相结合，从而在像素级别上过滤了不属于感兴趣的对象的不属于感兴趣的对象的策略。我们使用密集的SIFT描述符对CNN功能进行了自定义TOR图像数据集的SAKF，并使用密集的SIFT描述，并获得了87.98％的准确性，并且超过了所有其他方法。
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.
