中文摘要：

考虑到图像是一个局部平稳信源,提出一种局部内容自适应的LSB（least significant bit）匹配隐写分析方法.该方法将LSB匹配隐写建模为加性高斯噪声,将图像空域细节分量建模为高斯混合模型.在局部区域内用期望最大化算法估计模型参数,取最小方差值为局部隐写噪声方差的估计.然后提取局部方差直方图的加权和特征,以反映图像不同复杂度区域隐写前后的变化.将原始特征和校准特征相结合,作为分类特征.对未压缩图像库的实验表明,该方法较现有方法具有更好的检测性能,在嵌入率低至25%时仍有较可靠的检测性能.

英文摘要：

Based on a local stationary model of images,we propose a locally adaptive method to combat the least significant bit（LSB） matching steganography.We model the LSB matching embedding as additive Gaussian noise,use the Gaussian mixture model to describe local detail components,and estimate the model parameters with the expectation maximization algorithm.The smallest variance is selected as an estimation of local stego-noise variance.The weighted sum features of the local variance histogram are extracted to characterize changes in regions with different complexity between the cover and stego images.Features extracted from an image and its down-sampled version are combined and sent into a classifier.The experimental results on two sets of uncompressed images show that the proposed steganalyzer outperforms the prior art and provides reliable results for embedding rates as low as 0.25 bits per pixel.