In social media, location registration is emerging as a trend. Concurrently, the geo-position locations are modified by the criminals based on their needs. Thus, it is necessary to identify the authenticity of geo-position. The existing method failed in detecting the prior information and is effective in small datasets. Hence, a multi-aided deep residual network spoof detect (multi-aided DRN_Spoof detect) is developed for the detection of spoofed images in this research. The multi-aided DRN is utilized for residual noise extraction from the accumulated input image. Then, a fuzzy filter is utilized for camera footprint extraction from the residual noise extracted image, and the process is followed for spoof image. Moreover, the RV coefficient is applied for the fusion of the resultant extracted camera footprints. Later, Neyman similarity is applied to the fusion of geotagged images. At last, the spoofed image is identified from the resultant fused camera footprints and fused geotagged images using Motyka similarity. Further, the analysis shows that the multi-aided DRN_Spoof detect attained a true positive rate (TPR), accuracy, and true negative rate (TNR) of 95.79, 93.79, and 92.68%.