A number of approaches for personal authentication using palmprint features have been proposed in the literature, majority of which focus on improving the matching performance. However, of late, preventing potential attacks on biometric systems has become a major concern as more and more biometric systems get deployed for wide range of applications. Among various types of attacks, sensor level attack, commonly known as spoof attack, has emerged as the most common attack due to simplicity in its execution. In this paper, we present an approach for detection of display and print based spoof attacks on palmprint verifcation systems. The approach is based on the analysis of acquired hand images for estimating surface re ectance. First and higher order statistical features computed from the distributions of pixel intensities and sub-band wavelet coeefficients form the feature set. A trained binary classifier utilizes the discriminating information to determine if the acquired image is of real hand or a fake one. Experiments are performed on a publicly available hand image dataset, containing 1300 images corresponding to 230 subjects. Experimental results show that the real hand biometrics samples can be substituted by the fake digital or print copies with an alarming spoof acceptance rate as high as 79.8%. Experimental results also show that the proposed spoof detection approach is very effective for discriminating between real and fake palmprint images. The proposed approach consistently achieves over 99% average 10-fold cross validation classification accuracy in our experiments.