StyleStegan: Leak-free Style Transfer Based on Feature Steganography
Xiujian Liang Bingshan Liu Qichao Ying Zhenxing Qian Xinpeng Zhang
In modern social networks, existing style transfer methods suffer from a
serious content leakage issue, which hampers the ability to achieve serial and
reversible stylization, thereby hindering the further propagation of stylized
images in social networks. To address this problem, we propose a leak-free
style transfer method based on feature steganography. Our method consists of
two main components: a style transfer method that accomplishes artistic
stylization on the original image and an image steganography method that embeds
content feature secrets on the stylized image. The main contributions of our
work are as follows: 1) We identify and explain the phenomenon of content
leakage and its underlying causes, which arise from content inconsistencies
between the original image and its subsequent stylized image. 2) We design a
neural flow model for achieving loss-free and biased-free style transfer. 3) We
introduce steganography to hide content feature information on the stylized
image and control the subsequent usage rights. 4) We conduct comprehensive
experimental validation using publicly available datasets MS-COCO and Wikiart.
The results demonstrate that StyleStegan successfully mitigates the content
leakage issue in serial and reversible style transfer tasks. The SSIM
performance metrics for these tasks are 14.98% and 7.28% higher, respectively,
compared to a suboptimal baseline model.