本文提出了一种基于主成分分析（PCA, principal components analysis）及支持向量机（SVM, support vector machines）的信息隐藏盲检测方法。该方法根据信息隐藏时对载体图像引入噪声的特点，通过分析图像块的主成分，计算出图像的特征向量。通过对原始样本图像和藏密样本图像特征向量的学习和训练，得到SVM检测模型，可用于信息隐藏的盲检测。实验结果表明，该方法能够有效地检测出目前常用的信息隐藏方法。

This paper presents a method for steganalysis of images based on principal component analysis and support vector machine. Considering that embedding information inevitably brings noise to cover images, we design and calculate eigenvectors of images after analyzing principal components of image blocks. By using eigenvectors of cover images and steg-images to train SVM, we can obtain a trained SVM to be used to blind detection. Experimental results show that the proposed scheme is effective to many steganographic methods and correct detection rate is better than the steganalysis method using image quality metrics.