The steganalysis/detection of spatial domain least significant bit (LSB) matching steganography in grayscale images, which is the antetype of many sophisticated steganographic methods, is concentrated on. Spatial LSB matching can be modeled by adding independent noise to the image, and it is proved theoretically that the LSB matching will smooth the image histogram and histogram of difference image. Accordingly, the absolute differences between adjacent elements of image histogram are calculated as the histogram features, and co-occurrence matrix is utilized to extract features based on image correlation. A calibrated image is generated by embedding a message into the pending image. The features are extracted from both pending and calibrated images, and the ratios of corresponding features between pending and calibrated images are used as the final features. A support vector machine is utilized to train the classifier with the extracted features. Experimental results show that the proposed features outperform some previous ones and reveal the respective strong points of histogram and correlation features in the detection of never-compressed and JPEG-compressed images.