基于图像分类的鲁棒无载体信息隐藏

针对传统信息隐藏方法须通过修改载体以嵌入秘密信息所带来的安全性问题,提出一种基于图像分类与尺度不变特征转换(scale-invariant feature transform,SIFT)提取无载体信息隐藏的方法。首先通过快速区域卷积神经网络将原始图像库进行分类处理,生成不同种类的子图像库;然后利用图像SIFT特征点的方向信息设计一个感知鲁棒的哈希方案,并使用该方案计算出每个子图像库中图像的哈希值,将所有子图像库中的图像全部映射成相应的二进制哈希值;最后将秘密信息分割成若干个片段,通过对比秘密信息片段与所有的图像二进制哈希值,从子图像库中检索出与秘密信息片段相符的图像,将其作为含密图像传送给接收方,完成信息隐藏过程。接收方接收到全部含密图像后,根据约定的哈希方案提取秘密信息。实验结果和分析表明,该方法对JPEG压缩、高斯噪声、椒盐噪声、图像缩放等攻击具有较强的鲁棒性,且隐藏容量较高。

Robust Coverless Information Hiding Based on Image Classification

Aiming at the problem that traditional information hiding is difficult to resist the detection of steganalysis algorithms, this paper proposes a coverless data hiding method based on image classification and scale-invariant feature transform (SIFT) extraction. Firstly, an original image database is classified by using Faster R-CNN to generate different kinds of sub-image databases. Secondly, a robust hashing scheme is designed by using the direction information of SIFT feature points of the image, with which the image hash value of each sub-image database is calculated, and all images in each sub-image databases are mapped to corresponding binary hash values. Finally, secret information is divided into several segments, and by comparing the secret information segments with the binary hash values of all the images, the images corresponding to the secret information segments are retrieved from the sub-image database. These images are transmitted to the receiver as the carrier containing the secret to complete the information hiding process. The receiver receives all the stego-images, and extracts the secret information according to the agreed hashing scheme. Experimental results and analysis show that the proposed method is robust to JPEG compression, Gaussian noise, salt and pepper noise, image scaling and other attacks, and the performance of hiding capacity is also improved.