Robust image recapture detection using a K-SVD learning approach to train dictionaries of edge profiles
A professionally recaptured image from an LCD monitor can be, visually, very difficult to distinguish from its original counterpart. In this paper we show that it is possible to detect a recaptured image from the unique nature of the edge profiles present in the image. We leverage the fact that the edge profiles of single and recaptured images are markedly different and we train two alternative dictionaries using the K-SVD approach. One dictionary is trained to provide a sparse representation of single captured edges and a second for recaptured edges. Using these two learned dictionaries, we can determine whether a query image has been recaptured. We achieve this by observing the type of dictionary that gives the smallest error in a sparse representation of the edges of the query image. Experiments conducted show that the proposed algorithm is capable of detecting recaptured images with a high level of accuracy and copes well with a wide range of natural images.