@proceedings {329, title = {It is double pleasure to deceive the deceiver: disturbing classifiers against adversarial attacks}, journal = {2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)}, year = {2020}, pages = {160-165}, abstract = {

Convolutional neural networks (CNNs) for image classification can be fragile to small perturbations in the images they ought to classify. This fragility exposes CNNs to malicious attacks, resulting in safety concerns in many application domains. In this paper, we propose a simple yet efficient strategy for decreasing the effectiveness of black-box attacks that need to sequentially query the classifier network in order to build an attack. The general idea consists of applying controlled random disturbances (noise) at the softmax output layer of neural network classifiers, changing the confidence scores according to a set of design requirements. To evaluate this defense strategy, we employ a CNN, trained on the MNIST data set, and attack it with a black-box attack method from the literature called ZOO. The results show that our defense strategy: a) decreases the attack success rate of the adversarial examples; and b) forces the attack algorithm to insert larger perturbations in the input images.

}, doi = {10.1109/SMC42975.2020.9282889}, author = {Jo{\~a}o G. Zago and Eric A. Antonelo and Fabio L. Baldissera and Rodrigo T. Saad} } @proceedings {312, title = {On importance weighting for electric fraud detection with dataset shifts}, journal = {IEEE International Conference on Systems, Man, and Cybernetics (SMC)}, year = {2019}, month = {2019}, pages = {3215-3222}, abstract = {

Covariate shift and imbalanced datasets are common in real-world scenarios. Usually, the probability distribution for the collected data\  is non-stationary due to\  the incremental and endless process of sequential data collection, which is influenced by actions and predictions of human experts, predictive models, set of rules or other unknown external factors (i.e., user interaction on a website, seasonal/cyclic or geographical factors). Thus, a predictive model may be suboptimal in terms of generalization performance under a shift in the test input. In this work, we evaluate the importance-weighted fisher discriminant analysis (FDA) classifier in an electric fraud detection task with dataset shift, where the goal is to detect customers with frauds or irregular eletricity meters, also called nontechnical loss detection in the literature.
\ The inputs to the model are mainly based on features computed from the monthly energy consumption time series of each customer, using a real-world dataset of 3.6M clients from an energy distribution network.
The importance weights which define the relevance of each training input sample are estimated via either of two methods: the Kullback-Leibler importance estimation procedure (KLIEP) and another based on a discriminative classifier with probabilistic output.\ 
On a series of experiments, we show that a misspecified (biased) classifer in the form of a Least Squares solution has its bias removed when the estimated importance weights are employed in the model, making it comparable to the solution given by the original unbiased FDA for the electric fraud detection task.

}, keywords = {covariate shift, electric fraud detection, importance weighting}, author = {Eric A Antonelo and Radu State} } @proceedings {Antonelo2008a, title = {Imitation Learning of an Intelligent Navigation System for Mobile Robots using Reservoir Computing}, journal = {Proceedings of the 10th Brazilian Symposium on Neural Networks (SBRN)}, year = {2008}, pages = {93-98}, abstract = {

The design of an autonomous navigation system for mobile robots can be a tough task. Noisy sensors, unstructured environments and unpredictability are among the problems which must be overcome. Reservoir Computing (RC) uses a randomly created recurrent neural network (the reservoir) which functions as a temporal kernel of rich dynamics that projects the input to a high dimensional space. This projection is mapped into the desired output (only this mapping must be learned with standard linear regression methods). In this work, RC is used for imitation learning of navigation behaviors generated by an intelligent navigation system in the literature. Obstacle avoidance, exploration and target seeking behaviors are reproduced with an increase in stability and robustness over the original controller. Experiments also show that the system generalizes the behaviors for new environments.

}, issn = {978-0-7695-3361-2}, doi = {10.1109/SBRN.2008.32}, url = {https://ieeexplore.ieee.org/document/4665898/}, author = {Eric A Antonelo and Benjamin Schrauwen and Dirk Stroobandt} } @proceedings {antonelo2005, title = {Intelligent autonomous navigation for mobile robots: spatial concept acquisition and object discrimination}, journal = {Proceedings of the 6th IEEE International Symposium on Computational Intelligence in Robotics and Automation}, year = {2005}, pages = {553-557}, publisher = {IEEE}, address = {Helsinki, Finland}, abstract = {

An autonomous system able to construct its own navigation strategy for mobile robots is proposed. The navigation strategy is molded from navigation experiences (succeeding as the robot navigates) according to a classical reinforcement learning procedure. The autonomous system is based on modular hierarchical neural networks. Initially the navigation performance is poor (many collisions occur). Computer simulations show that after a period of learning the autonomous system generates efficient obstacle avoidance and target seeking behaviors. Experiments also offer support for concluding that the autonomous system develops a variety of object discrimination capability and of spatial concepts.

}, issn = {0-7803-9355-4}, doi = {10.1109/CIRA.2005.1554335}, url = {https://ieeexplore.ieee.org/document/1554335/}, author = {Eric A Antonelo and Mauricio Figueiredo and Albert-Jan Baerlvedt and Rodrigo Calvo} }