2021/11/24 by Linfeng Ye, Ye, Linfeng, Hamidi, Shayan Mohajer · 1 citation
Computer Science · #92B20 #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #I.2.m #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2111.12305
openalex publication_date 2021/11/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The existing work shows that the neural network trained by naive gradient-based optimization method is prone to adversarial attacks, adds small malicious on the ordinary input is enough to make the neural network wrong. At the same time, the attack against a neural network is the key to improving its robustness. The training against adversarial examples can make neural networks resist some kinds of adversarial attacks. At the same time, the adversarial attack against a neural network can also reveal some characteristics of the neural network, a complex high-dimensional non-linear function, as discussed in previous work. In This project, we develop a first-order method to attack the neural network. Compare with other first-order attacks, our method has a much higher success rate. Furthermore, it is much faster than second-order attacks and multi-steps first-order attacks.