Glyph perturbation adjusts the characters’ structures and strokes to make the original characters change subtly, which cannot be detected by the naked eye. These generated variants with different glyph perturbation can represent different status of secret messages, which can be used to embed information in Chinese text documents. However, Chinese characters have characteristics in large numbers, complex structures, and diverse fonts, which limit the generation of glyph perturbation and make the design of Chinese characters time-consuming and laborious. Many font style transfer methods for Chinese characters have been proposed to improve the efficiency of Chinese character generation based on deep learning. At present, there are few studies on efficient font style transfer for glyph perturbation of Chinese characters. In this paper, a stylized glyph perturbation method based on style extractor and attention augmented convolution is proposed. It adopts a multi-head attention mechanism to enhance convolution in the font transfer, which concatenates the convolution feature maps and the self-attention activation maps to weaken the limitations of ordinary convolution in processing images. The extracted style features are sent into the decoder of the font transfer network so as to improve the stylized ability. Particularly, the impact of style extractor and attention augmented convolution on the glyph perturbation generation is addressed. The extraction accuracy and embedding capacity are tested in our experiments. The embedding capacity of secret message can achieve around 1.8 bit/character.