基于图像分类的无载体信息隐藏方法

Coverless Information Hiding Algorithm Based on Image Classification

为提高无载体信息隐藏的数据嵌入容量和通信效率,注意到半构造式无载体信息隐藏算法所具有的优势,在仔细分析几种社交平台的用户行为习惯后,提出了一种以社交平台的行为习惯为构造原则的半构造式无载体信息隐藏算法.该算法的具体思想通过构建小图标库中的图标与秘密消息的一一映射关系,将小图标按照一定的原则拼接,完成秘密消息的图像表达,通过传递拼接好的图片,实现秘密消息的传递.为了提高小图标的识别率和整个隐蔽通信系统的抗干扰能力,算法还引入了卷积神经网络对图标库中的图标进行训练和分类,同时在训练时特意引入经过多种攻击方式处理过的小图标作为干扰样本.实验和仿真结果表明,该隐藏方法具备良好的抗攻击能力,隐藏容量和通信效率得到了实质性的提高,可用于实际的隐蔽通信系统.

In order to improve the data embedding capacity and the communication efficiency of coverless information hiding algorithm,addressing the advantages of semi-structured coverless information hiding algorithm,this paper introduces a semi-structured coverless information hiding algorithm based on the behavioral habits of social platforms.The specific idea of the algorithm is to build a one-to-one mapping relationship between icons and secret messages in a small icon library.According to certain principles,some small icons are montaged a picture,the secret information can be expressed by the splicing picture,and the transmission of secret messages is realized by delivering the spliced pictures.In order to improve the recognition rate of small icons and the anti-interference ability of the whole hidden communication system,convolutional neural network is also introduced to train and classify the icons in the icon library,and the interference samples are introduced as training samples set.The experimental results show that the algorithm has good anti-attack ability and the hiding capacity can be improved,and therefore,the algorithm can be used in covert communication.