基于图像Bag-of-Words模型的无载体信息隐藏

Coverless Information Hiding Based on Bag-of-Words Model of Image

介绍一种基于bag-of-words(BOW)模型的无载体信息隐藏方法.该方法使用BOW模型提取图像的视觉关键词(visual words,VW)以表达待隐藏的文本信息,从而实现文本信息在图像中的隐藏.首先使用BOW模型提取图像集中每幅图像的VW,构建文本信息的关键词和VW的映射关系库;然后把每幅图像分为若干子图像,统计每一幅子图像的VW频数直方图,选择频数最高的VW表示该子图像;最后根据构建的文本关键词和子图像VW的映射关系库,搜索出与待隐藏文本信息存在映射关系的子图像序列,将含有这些子图像的图像作为含密图像进行传递.实验结果和分析表明,该隐藏算法在抗隐写分析、鲁棒性和安全性方面均有良好的表现.

This paper introduces a coverless information hiding method based on the bagof-words model（BOW）.To hide text information into an image,visual words are extracted to represent the text information.Visual words from an image set are extracted using a BOW model,and a mapping relation between keywords in the text information and visual words is established.Each image is then divided into several sub-images.For each sub-image,a histogram of visual words is computed,and visual words having the largest values in the histogram selected to represent the sub-image.According to the mapping relation,a set of sub-images with visual words related to the text information is found.The images containing these sub-images are used as stego-images for secret communication.The experimental results and analysis show that the proposed method has good performance in anti-steganalysis capability,robustness against common attacks,and security.