The invention puts forward a fuzzy detection method for SVD (Singular Value Decomposition) on the basis of an image DCT (Discrete Cosine Transform) domain. The method comprises the following steps that: firstly, calculating the gradient map of an image to be detected, wherein the edge information of the image can be obtained from the gradient map; then, carrying out partitioning on the gradient map, and carrying out DCT, wherein the alternating current coefficient of the DCT domain reflects the edge and the definition of the image; then, analyzing the alternating current coefficient information of the DCT domain by a difference matrix, calculating the singular value of the difference matrix, and constructing a response function to express the fuzzy degree of the image of a block; and finally, using a mean value and a variance to carry out normalization on an image block response sum to eliminate the influence of image contents. An experiment indicates that the fuzzy score obtained by the method is highly consistent with the subjective evaluation score of a human eye for the image. The detection model disclosed by the invention considers the characteristics that the edge becomes wide, the definition becomes weak and the like in an image blurring process, the influence of image contents is effectively eliminated, so that detection accuracy is high, in addition, detection efficiency is high, and integral performance is superior to the integral performance of a previous method.