Abstract
Counterfeit detection in official documents has challenged forensic experts on trying to correlate them to improve the identification of forgery authors by criminal investigators. Past counterfeit investigation on the Portuguese Police Forensic Laboratory allowed the construction of an organized set of digital images related to counterfeited documents, helping manual identification of new counterfeiters modus operandi. However, these images are usually stored in distinct resolutions, may have different sizes and could have been captured under different types of illumination. In this paper we present a methodology to automate a counterfeit identification modus operandi, by comparing a given document image with a database of previously catalogued counterfeited documents images. The proposed method ranks the identified counterfeited documents and allows the forensic experts to drive their attention to the most similar documents. It takes advantage of scalable algorithms under the OpenCV framework that compare images, match patterns and analyse textures and colours. We present a set of tests with distinct datasets with promising results.

Abstract
Ensemble approaches have revealed remarkable abilities to tackle different learning challenges, namely in dynamic scenarios with concept drift, e.g. in social networks, as Twitter. Several efforts have been engaged in defining strategies to combine the models that constitute an ensemble. In this work, we investigate the effect of using different metrics for combining ensembles' models, specifically performance-based metrics. We propose five performance combining metrics, having in mind that we may take advantage of diversity in classifiers, as their individual performance takes a leading role in defining their contribution to the ensemble. Experimental results on a Twitter dataset, artificially timestamped, suggest that using performance metrics to combine the models that constitute an ensemble can introduce relevant improvements in the overall ensemble performance.

Abstract
Nowadays most learning problems demand adaptive solutions. Current challenges include temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. Various efforts have been pursued in machine learning settings to learn in such environments, specially because of their non-trivial nature, since changes occur between the distribution data used to define the model and the current environment. In this work we present the Drift Adaptive Retain Knowledge (DARK) framework to tackle adaptive learning in dynamic environments based on recent and retained knowledge. DARK handles an ensemble of multiple Support Vector Machine (SVM) models that are dynamically weighted and have distinct training window sizes. A comparative study with benchmark solutions in the field, namely the Learn + +.NSE algorithm, is also presented. Experimental results revealed that DARK outperforms Learn + +.NSE with two different base classifiers, an SVM and a Classification and Regression Tree (CART).

Abstract
Criminal investigations face a deluge of structured and unstructured data obtained from heterogeneous sources like forensic reports or wiretap transcriptions. In these cases, finding relevant information can be a complex task. Ontologies have been successfully applied to several domains including legal, cyber crime and digital forensics. In this paper it is proposed a framework based on ontology engineering, that provides an unified approach to represent and reason with the criminal investigation data. Moreover, this framework is applied to the specific use case of money laundering.

Abstract

Abstract