This paper presents a simple and efiective method to verify similar vehicle images.In order to provide a meaningful interpretation of the verification,we propose to detect the local difierences between two images.We frame this task as a saliency map regression problem,where the saliency map measures the degree of discrepancy at every pixel.To achieve this goal,we use a convolutional neural network(CNN)to map two aligned vehicle images to one saliency map.Our network design enables end-to-end training.We validate our algorithm on a vehicle image dataset.Experimental results show that our approach is accurate,fast and robust,and it achieves better performance than other methods.