Background fraud detection method and device and electronic equipment

The invention discloses a background fraud detection method and device and electronic equipment. The method comprises the following steps: inputting a group of pictures to be detected into a pre-trained deep neural network to extract depth local features of each picture; calculating an interior point between the depth local features of every two pictures by using a random sampling consensus algorithm to obtain an interior point calculation result, wherein the inner points are used for representing that the depth local features of the pictures have consistency; constructing an adjacent matrix based on an interior point calculation result, wherein elements in the adjacent matrix are the number of interior points between the depth local features of the picture; clustering the adjacent matrixes by using an affinity diffusion clustering algorithm to obtain a plurality of clustering clusters, wherein each clustering cluster comprises a plurality of elements; determining whether background fraud exists in the group