This article discusses the use of universal adversarial as well as to improve the effectiveness of protection systems against robots and spam. In particular, the key features that need to be taken into account to ensure an optimal level of protection against robots and spam are considered. It is also discussed why modern methods of protection are ineffective, and how the use of universal adversarial attacks can help eliminate existing shortcomings. The purpose of this article is to propose new approaches and methods of protection that can improve the effectiveness and stability of protection systems against robots and spam.
Keywords: machine learning, clustering, data recognition, library Nanonets, library Tesseract