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.
@article{arxiv.2005.10086,
title = {Classifying Suspicious Content in Tor Darknet},
author = {Eduardo Fidalgo Fernandez and Roberto Andrés Vasco Carofilis and Francisco Jáñez Martino and Pablo Blanco Medina},
journal= {arXiv preprint arXiv:2005.10086},
year = {2020}
}
Comments
To be published on the JNIC 2020 Conference. Summary of already published research