[FAPESP 2018/23908-1] Towards the robustness in deep learning architectures for e-Science applications

Deep learning architectures, in particular the convolutional neural networks (CNNs) are responsible for recent research advances in computational vision and machine learning areas. Due to the fact these networks have achieved excellent results in different application domains. In 2014, a Google research group found that several machine learning models were vulnerable to adversarial examples. The addition of imperceptible noise in images was enough to fool any trained machine learning model. This fact has leveraged a new research field, adversarial pattern recognition, which aims the creation of robust learning models to data distribution different from used in the training process (adversarial examples).