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.

翻译：在现代社交网络中，现有风格迁移方法存在严重的内容泄露问题，这阻碍了实现串行化与可逆的风格化操作，进而限制了风格化图像在社交网络中的进一步传播。为解决该问题，我们提出了一种基于特征隐写的无泄漏风格迁移方法。该方法包含两个核心组件：在原始图像上完成艺术风格化的风格迁移方法，以及在风格化图像上嵌入内容特征秘密的图像隐写方法。本研究的主要贡献如下：1）我们识别并解释了内容泄露现象及其根本原因——原始图像与后续风格化图像之间存在内容不一致性；2）设计了一种实现无损失、无偏置风格迁移的神经流模型；3）引入隐写技术将内容特征信息隐藏于风格化图像中，并控制后续使用权；4）使用公开数据集MS-COCO与Wikiart进行了全面的实验验证。结果表明，StyleStegan成功缓解了串行与可逆风格迁移任务中的内容泄露问题。与次优基线模型相比，这两项任务的SSIM性能指标分别提升了14.98%与7.28%。