Qualificação - Reconhecimento de Cenários baseado nas Localizações dos Fornecedores do Governo Federal
In most corruption scandals, the use of front companies for money laundering is almost ubiquitous. This work proposes to apply image classification to detect such organizations, through the use of Convolutional Neural Networks (CNN), namely the AlexNet architecture. The images are obtained by address search in Google Street View API, and the resulting classification will be further used along with other features to detect front com- panies in order to help the auditors from the Ministry of Transparency and Office of the Comptroller General (CGU, in Portuguese). To this moment, we applied classification to almost 15 thousand suppliers scenes with active contracts with the Brazilian Government until September 2016, obtained through data matching between the Government Purchases database and the Brazilian Federal Revenue Office database (more recent scenes should be added as this work progresses). Preliminary results with a pre-trained AlexNet CNN show the need for developing new scene classes more suited to the Brazilian context. In order to do this, we propose to apply clustering algorithms in features extracted from the last fully-connected layer of this net. The classes obtained will be used to fine-tune the AlexNet CNN for future classification, through the use of training from scratch or fine tuning techniques.
Rodrigo Peres Ferreira
MsC, PhD Tese UTAD
Modelo de documento para Dissertacao/Tese de Mestrado/Doutoramento em Engenharia Electrotecnica e de Computadores.
Raul Morais, 2007 - 2008
Manuel Cabral, 2008
Antonio Valente, 2004-2018