|
ИСТИНА |
Войти в систему Регистрация |
ФНКЦ РР |
||
A Survey of Adversarial Attacks and Defenses for image data on Deep Learningстатья
- Авторы: Huayu Li, Namiot Dmitry
- Журнал: International Journal of Open Information Technologies
- Том: 10
- Номер: 5
- Год издания: 2022
- Издательство: Лаборатория Открытых Информационных Технологий факультета ВМК МГУ им. М.В. Ломоносова
- Местоположение издательства: Москва
- Первая страница: 9
- Последняя страница: 16
- Аннотация: This article provides a detailed survey of the so-called adversarial attacks and defenses. These are special modifications to the input data of machine learning systems that are designed to cause machine learning systems to work incorrectly. The article discusses traditional approaches when the problem of constructing adversarial examples is considered as an optimization problem - the search for the minimum possible modifications of correlative data that ”deceive” the machine learning system. As tasks (goals) for adversarial attacks, classification systems are almost always considered. This corresponds, in practice, to the so-called critical systems (driverless vehicles, avionics, special applications, etc.). Attacks on such systems are obviously the most dangerous. In general, sensitivity to attacks means the lack of robustness of the machine (deep) learning system. It is robustness problems that are the main obstacle to the introduction of machine learning in the management of critical systems.
- Добавил в систему: Намиот Дмитрий Евгеньевич