Adversarial Attacks Technology in Deep Learning Models
Yucong Lai, Yifeng Wang · Journal of Physics Conference Series · 2021
Abstract Deep learning related to computer vision, speech recognition, and language processing has been developing rapidly over recent years. The applications of these models, however, have underlying risks. Recent studies have shown that small perturbation from adversarial examples could result in false interpretation of the neural network examples and false judgment. Therefore, understanding adversarial example technologies is essential for promoting the safety and robustness of neural network models. This paper summarizes current adversarial example technologies in different applications, discusses the current prospects and challenges, and envision potential future developments in related fields.