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Multiple classifier systems (MCSs) based on the combination of a set of different classifiers are currently used to achieve high pattern-recognition performances [1]. For each pattern, the classification process is performed in parallel by different classifiers and the results are then combined according to some decision “fusion” method (e.g., the(More)
In the field of pattern recognition, the combination of an ensemble of neural networks has been proposed as an approach to the development of high performance image classification systems. However, previous work clearly showed that such image classification systems are effective only if the neural networks forming them make different errors. Therefore, the(More)
In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data. In one pertinent, well-motivated attack scenario, an adversary may attempt to evade a deployed system at test time by carefully manipulating attack samples. In this work, we present a simple but effective gradientbased(More)
In recent years anti-spam filters have become necessary tools for Internet service providers to face up to the continuously growing spam phenomenon. Current server-side anti-spam filters are made up of several modules aimed at detecting different features of spam e-mails. In particular, text categorisation techniques have been investigated by researchers(More)
A spoof or fake is a counterfeit biometric that is used in an attempt to circumvent a biometric sensor. Liveness detection distinguishes between live and fake biometric traits. Liveness detection is based on the principle that additional information can be garnered above and beyond the data procured by a standard verification system, and this additional(More)
can be obtained using the so-called “reject” option. Namely, the patterns that are the most likely to be misclassified are rejected (i.e., they are not classified); they are then handled by more sophisticated procedures (e.g., a manual classification is performed). However, handling high reject rates is usually too time-consuming for application purposes.(More)
Multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed in the field of pattern recognition as a method for the development of high performance classification systems. Previous work clearly showed that multiple classifier systems are effective only if the classifiers forming them are accurate and(More)
In the field of pattern recognition, the concept of Multiple Classifier Systems (MCSs) was proposed as a method for the development of high performance classification systems. At present, the common “operation” mechanism of MCSs is the “combination” of classifiers outputs. Recently, some researchers pointed out the potentialities of “dynamic classifier(More)