Image copy detection is an art of searching duplicates from a target database. Computationally efficient and robust detection is still a challenging issue. Inspired by the recent study of sparsity in the context of compressed sensing, we propose a sparse representation-based image copy detection method exploiting sparsity as the cue for searching duplicates. We find that although sparse representation can describe an image in a compact manner, the inherent discriminable features, as far as we know, are not entirely explored. In this paper, we study the discrimination ability inherent in sparsity via online dictionary learning and compact feature descriptor representation. Experimental results show that our method, compared with state-of-the-art, is computationally efficient and attains better or comparable detection performance measured in terms of precision and recall rates.