Abstract: Deep learning is now widely used in areas such as computer vision, robotics, and natural language processing. However, it has been shown that deep neural networks are vulnerable to adversarial examples, and a single carefully crafted adversarial example can make deep learning models misjudge. Most of the existing studies mislead the adversarial attack on classifiers by generating a small perturbation of the Lp paradigm, but the results achieved are not satisfactory. In this paper, we propose a new adversarial attack method, colorization adversarial attack, which converts the input samples into grayscale maps, designs a grayscale coloring method to guide the grayscale map coloring, and finally uses the colorized images to deceive the classifier to achieve unrestricted attacks. Experiments show that the adversarial examples produced by this method performs well in deceiving several state-of-the-art deep neural network image classifiers and passes human perception research tests.

Key words: adversarial attack, colorization, adversarial examples, unrestricted attack