The invention discloses a method for identifying a natural image and a computer generated image based on DCT (Discrete Cosine Transformation)-domain statistic characteristics. The method is characterized by comprising the following steps of (1) firstly, carrying out gaussian fuzzy processing and dimension reduction processing on an image to be detected, carrying out 8*8 no-repeat DCT on3 channels R, G and B of the image to obtain a 8*8 partitioning DCT coefficient matrix; (2) carrying out statistics on the distribution of a first significant figure of the DCT-domain AC coefficient of each channel to obtain three probability distribution curves; and (3) calculating an average absolute differential of the three probability distribution curves, if the average absolute differential is greater than a set threshold T to prove that the overlapping degree of the three curves is not sufficient, judging the image to be detected as the natural image, otherwise, judging the image to be detected as the computer generated image. Experimental results show that due to the algorithm, the identification accuracy rate of the natural image and the computer generated image is increased. Compared with the existing algorithm, the method has the advantages that the identification rate is higher, the calculating amount is small, the implementation is easy, and the identification accuracy rate reaches 95.22%.