基于边信息估计的JPEG图像隐写方法

JPEG Image Steganography Based on Side Information Estimation

基于JPEG图像的隐写方法通常利用原始图像的边信息来提高隐写安全性.然而现有的JPEG图像隐写方法在使用边信息时需要提前获取未压缩的原始图像,当原始图像不可获得时,往往不能把边信息用于设计隐写方法.文中提出了一种在不具有未压缩的原始图像的情况下,通过估计图像的边信息来提高JPEG图像隐写安全性的方法.首先,使用卷积神经网络对JPEG载体图像进行恢复,得到估计的原始图像;其次,通过得到的估计图像计算取整误差;然后利用取整误差来控制嵌入过程中离散余弦变换(DCT)系数的修改方向;最后结合DCT系数的失真代价和STC编码来实现隐写嵌入.文中提出的边信息估计方法能够应用于现有的JPEG图像自适应隐写方法,如JC-UED和J-UNIWARD.实验结果表明,该方法能较好地恢复图像的边信息,并能显著提高现有JPEG图像自适应隐写方法的安全性.

JPEG steganography usually takes advantage of the side information of raw images to improve steganographic security.However,the raw images are indispensable for existing JPEG steganography to apply side information.When the raw images are not available,the side information cannot be used to design more secure steganography.In this paper,we propose to improve the steganographic security by estimating the side information from JPEG images when the raw images are not available.Firstly,the convolution neural networks are used to restore the estimated spatial images from JPEG images.Secondly,the rounding errors are calculated with the estimated images.Then,the rounding errors are utilized to control the modification directions of DCT coefficients in the embedding process.Finally,by integrating the selected modification directions and the distortion costs of DCT coefficients,the STC-based steganographic embedding is implemented.The proposed method can apply to existing adaptive JPEG steganographic schemes,such as JC-UED and J-UNIWARD.The experimental results show that the proposed me-thod can effectively estimate the side information from JPEG images and can improve the security of existing adaptive JPEG steganographic schemes.