Publications

@article{fidalgo_classifying_2020,

title = {Classifying Suspicious Content in Tor Darknet},

author = {Eduardo Fidalgo and Andrés Carofilis-Vasco and Francisco Jáñez-Martino and Pablo Blanco-Medina},

url = {https://arxiv.org/abs/2005.10086},

year  = {2020},

date = {2020-01-01},

journal = {arXiv e-prints},

pages = {arXiv–2005},

abstract = {This paper proposes Semantic Attention Keypoint Filtering (SAKF) to classify Tor Darknet images by focusing on significant features related to criminal activities. By combining saliency maps with Bag of Visual Words (BoVW), SAKF outperforms CNN approaches (MobileNet v1, ResNet50) and BoVW with dense SIFT descriptors, achieving 87.98% accuracy.},

keywords = {Computer vision, Criminal Activity Detection, Darknet Analysis, Image classification},

pubstate = {published},

tppubtype = {article}

}

@article{fidalgo_classifying_2020-1,

title = {Classifying suspicious content in Tor Darknet},

author = {Eduardo Fidalgo and Andrés Carofilis-Vasco and Francisco Jáñez-Martino and Pablo Blanco-Medina},

url = {https://ui.adsabs.harvard.edu/abs/2020arXiv200510086F/abstract},

year  = {2020},

date = {2020-01-01},

journal = {arXiv preprint arXiv:2005.10086},

abstract = {This paper proposes Semantic Attention Keypoint Filtering (SAKF) to classify Tor Darknet images by focusing on significant features related to criminal activities. By combining saliency maps with Bag of Visual Words (BoVW), SAKF outperforms CNN approaches (MobileNet v1, ResNet50) and BoVW with dense SIFT descriptors, achieving 87.98% accuracy.},

keywords = {Computer vision, Criminal Activity Detection, Darknet Analysis, Image classification},

pubstate = {published},

tppubtype = {article}

}