Publications
2019
1.
Fidalgo, Eduardo; Alegre, Enrique; Fernández-Robles, Laura; González-Castro, Víctor
Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering Artículo de revista
En: Digital Investigation, vol. 30, pp. 12–22, 2019, (Publisher: Elsevier).
Resumen | Enlaces | BibTeX | Etiquetas: Computer vision, Darknet Investigation, Image classification, Semantic Attention
@article{fidalgo_classifying_2019,
title = {Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering},
author = {Eduardo Fidalgo and Enrique Alegre and Laura Fernández-Robles and Víctor González-Castro},
url = {https://www.sciencedirect.com/science/article/pii/S1742287619300027},
year = {2019},
date = {2019-01-01},
journal = {Digital Investigation},
volume = {30},
pages = {12–22},
abstract = {This paper presents Semantic Attention Keypoint Filtering (SAKF) for automatically classifying relevant parts of images from the Tor Darknet, focusing on salient features while removing non-significant background. By combining saliency maps with Bag of Visual Words (BoVW), SAKF outperforms dense SIFT descriptors and deep CNN features (MobileNet, ResNet50), achieving significantly higher accuracies across multiple datasets. The approach shows promise for aiding law enforcement investigations in the Darknet.},
note = {Publisher: Elsevier},
keywords = {Computer vision, Darknet Investigation, Image classification, Semantic Attention},
pubstate = {published},
tppubtype = {article}
}
This paper presents Semantic Attention Keypoint Filtering (SAKF) for automatically classifying relevant parts of images from the Tor Darknet, focusing on salient features while removing non-significant background. By combining saliency maps with Bag of Visual Words (BoVW), SAKF outperforms dense SIFT descriptors and deep CNN features (MobileNet, ResNet50), achieving significantly higher accuracies across multiple datasets. The approach shows promise for aiding law enforcement investigations in the Darknet.