From the texture analysis of image content, we propose a steganalytic method to detect spatial domain steganography in grayscale images. First of all, based on the local linear vectors, which are selected carefully and sensitive to image texture, images are decomposed into several textural detail subbands by the local linear transform (LLT). Then the statistical distribution of the LLT coefficient is modeled by using the generalized Gaussian distribution. Finally, novel textural features of the LLT coefficient histogram and cooccurrence matrix are extracted for steganalyzers implemented by the support vector machine. Extensive experiments are performed on four diverse uncompressed image databases and seven typical spatial domain steganographic algorithms, such as the highly undetectable stego. The results reveal that the proposed scheme is universal for detecting spatial domain steganography. By comparison with other well-known feature sets, our presented feature set offers the best performance under most circumstances.