It has become easier to change the content of computerized photos as mixed media innovation has advanced. This is due to the wide availability of picture editing apps. If altered for a negative motive, created images can cause major social and legal problems. The detection of picture fraud requires the advancement of contemporary methods that can effectively spot modifications in sophisticated images. In photographs, joining mimicry is frequently used to hide the truth. Grafting creates sharp distinctions in the edges, corners, and smooth areas. Using picture grafting, discrete wavelet change, and histograms of discriminative vigorous neighborhood double designs, we proposed a novel method for finding picture imitations. A given color image is first converted to “YCbCr”, and the Cr and Cb components of the computerized image are then linked to DWT. DRLBP is used to show surface variation in each DWT subband. The final highlight trajectory is created by concatenating the DRLBP from every sub-band. After all, an SVM is used to produce an image fraud recognition demonstration. On openly available benchmark datasets, the suggested strategy's application and generalizability were evaluated. With 98.95% location accuracy, the suggested method outperformed cutting-edge imitation location techniques.