  In recent years, the security concerns about the vulnerability of Deep
Convolutional Neural Networks (DCNN) to Adversarial Attacks (AA) in the form of
small modifications to the input image almost invisible to human vision make
their predictions untrustworthy. Therefore, it is necessary to provide
robustness to adversarial examples in addition to an accurate score when
developing a new classifier. In this work, we perform a comparative study of
the effects of AA on the complex problem of art media categorization, which
involves a sophisticated analysis of features to classify a fine collection of
artworks. We tested a prevailing bag of visual words approach from computer
vision, four state-of-the-art DCNN models (AlexNet, VGG, ResNet, ResNet101),
and the Brain Programming (BP) algorithm. In this study, we analyze the
algorithms' performance using accuracy. Besides, we use the accuracy ratio
between adversarial examples and clean images to measure robustness. Moreover,
we propose a statistical analysis of each classifier's predictions' confidence
to corroborate the results. We confirm that BP predictions' change was below
2\% using adversarial examples computed with the fast gradient sign method.
Also, considering the multiple pixel attack, BP obtained four out of seven
classes without changes and the rest with a maximum error of 4\% in the
predictions. Finally, BP also gets four categories using adversarial patches
without changes and for the remaining three classes with a variation of 1\%.
Additionally, the statistical analysis showed that the predictions' confidence
of BP were not significantly different for each pair of clean and perturbed
images in every experiment. These results prove BP's robustness against
adversarial examples compared to DCNN and handcrafted features methods, whose
performance on the art media classification was compromised with the proposed
perturbations.
