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.

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