Vulnerability of classifiers to evolutionary generated adversarial examples. (July 2020)

• Record Type: Journal Article
• Authors: Vidnerová, Petra; Neruda, Roman
• Is Part Of: Neural networks. Volume 127(2020)
• Page Start: 168
• Page End: 181
• Publication Date: 2020-07
• Subjects: Supervised learning -- Neural networks -- Kernel methods -- Genetic algorithms -- Adversarial examples; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2020.04.015
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: This paper deals with the vulnerability of machine learning models to adversarial examples and its implication for robustness and generalization properties. We propose an evolutionary algorithm that can generate adversarial examples for any machine learning model in the black-box attack scenario. This way, we can find adversarial examples without access to model's parameters, only by querying the model at hand. We have tested a range of machine learning models including deep and shallow neural networks. Our experiments have shown that the vulnerability to adversarial examples is not only the problem of deep networks, but it spreads through various machine learning architectures. Rather, it depends on the type of computational units. Local units, such as Gaussian kernels, are less vulnerable to adversarial examples.