Analysis of Vulnerabilities of Neural Network Image Recognition Technologies

Abstract

The problem of vulnerability of artificial intelligence technologies based on neural networks is considered. It is shown that the use of neural networks generates a lot of vulnerabilities. Examples of such vulnerabilities are demonstrated, such as incorrect classification of images containing adversarial noise or patches, failure of recognition systems in the presence of special patterns in the image, including those applied to objects in the real world, training data poisoning, etc. Based on the analysis, the need to improve the security of artificial intelligence technologies is shown, and some considerations that contribute to this improvement are discussed.