Educational and methodological materials of the master class “Adversarial attacks on image recognition neural networks” for students and schoolchildren

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The problem of neural network vulnerability has been the subject of scientific research and experiments for several years. Adversarial attacks are one of the ways to “trick” a neural network, to force it to make incorrect classification decisions. The very possibility of adversarial attack lies in the peculiarities of machine learning of neural networks. The article shows how the properties of neural networks become a source of problems and limitations in their use. The materials of the corresponding researches of the author were used as a basis for the master class “Adversarial attacks on image recognition neural networks”.The article presents the educational materials of the master class: the theoretical background of the class, practical materials (in particular, the attack on a single neuron is described, the fast gradient sign method for attacking a neural network is considered), examples of experiments and calculations (the author uses the convolutional network VGG, Torch and CleverHans libraries), as well as a set of typical errors of students and the teacher’s explanations of how to eliminate these errors. In addition, the result of the experiment is given in the article, and its full code and examples of approbation of the master class materials are available at the above links.The master class is intended for both high school and university students who have learned the basics of neural networks and the Python language, and can also be of practical interest to computer science teachers, to developers of courses on machine learning and artificial intelligence as well as to university teachers.