Neural Network Adversarial Attack Method Based on Improved Genetic Algorithm

Deep learning algorithms are widely used in fields such as computer vision and natural language processing, but they are vulnerable to security threats from adversarial attacks because of their internal presence of a large number of nonlinear functions and parameters leading to their uninterpretability. In this paper, we propose a neural network adversarial attack method based on an improved genetic algorithm. The improved genetic algorithm improves the variation and crossover links based on the original genetic optimization algorithm, which greatly improves the iteration efficiency and shortens the running time. The method does not need the internal structure and parameter information of the neural network model, and it can obtain the adversarial samples with high confidence in a short time by the classification and confidence information of the neural network. The experimental results show that the method in this paper has a wide range of applicability and high efficiency for the model, and provides a new idea for the adversarial attack.