Classifying Suspicious Content in Tor Darknet
This addresses the time-consuming manual inspection of Darknet images for criminal evidence, though it is incremental as it builds on existing computer vision techniques.
The paper tackled the problem of automatically classifying suspicious content in Tor Darknet images to aid law enforcement, achieving 87.98% accuracy with their Semantic Attention Keypoint Filtering method, which outperformed CNN and BoVW baselines.
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.